Neural generative dialogue agents have shown an increasing ability to hold short chitchat conversations, when evaluated by crowdworkers in controlled settings. However, their performance in real-life deployment – talking to intrinsically-motivated users in noisy environments – is less well-explored. In this paper, we perform a detailed case study of a neural generative model deployed as part of Chirpy Cardinal, an Alexa Prize socialbot. We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition. However, even in unambiguous contexts the model frequently makes reasoning errors. Though users express dissatisfaction in correlation with these errors, certain dissatisfaction types (such as offensiveness and privacy objections) depend on additional factors – such as the user’s personal attitudes, and prior unaddressed dissatisfaction in the conversation. Finally, we show that dissatisfied user utterances can be used as a semi-supervised learning signal to improve the dialogue system. We train a model to predict next-turn dissatisfaction, and show through human evaluation that as a ranking function, it selects higher-quality neural-generated utterances.

We collected a corpus of human-human task-oriented dialogs rich in dissatisfaction and built a model that used prosodic features to predict when the user was likely dissatisfied. For utterances this attained a F.25 score of 0.62,against a baseline of 0.39. Based on qualitative observations and failure analysis, we discuss likely ways to improve this result to make it have practical utility.

Real-world conversational agents must effectively handle long conversations that span multiple contexts. Such context can be interspersed with chitchat (dialog turns not directly related to the task at hand), and potentially grounded in a multimodal setting. While prior work focused on the above aspects in isolation, there is a lack of a unified framework that studies them together. To overcome this, we propose DialogStitch, a novel framework to seamlessly ‘stitch’ multiple conversations and highlight these desirable traits in a taskoriented dialog. After stitching, our dialogs are provably deeper, contain longer-term dependencies, and span multiple contexts, when compared with the source dialogs—all free of cost without any additional annotations! Though our framework generalizes to a variety of combinations, we demonstrate its benefits in two settings: (a) multimodal, imagegrounded conversations, and, (b) task-oriented dialogs fused with chit-chat conversations. We benchmark state-of-the-art dialog models on our datasets and find accuracy drops of (a) 12% and (b) 45% respectively, indicating the additional challenges in the stitched dialogs. Our code and data are publicly available.

here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.

While named entity recognition (NER) from speech has been around as long as NER from written text has, the accuracy of NER from speech has generally been much lower than that of NER from text. The rise in popularity of spoken dialog systems such as Siri or Alexa highlights the need for more accurate NER from speech because NER is a core component for understanding what users said in dialogs. Deployed spoken dialog systems receive user input in the form of automatic speech recognition (ASR) transcripts, and simply applying NER model trained on written text to ASR transcripts often leads to low accuracy because compared to written text, ASR transcripts lack important cues such as punctuation and capitalization. Besides, errors in ASR transcripts also make NER from speech challenging. We propose two models that exploit dialog context and speech pattern clues to extract named entities more accurately from open-domain dialogs in spoken dialog systems. Our results show the benefit of modeling dialog context and speech patterns in two settings: a standard setting with random partition of data and a more realistic but also more difficult setting where many named entities encountered during deployment are unseen during training.

We propose a novel on-device neural sequence labeling model which uses embedding-free projections and character information to construct compact word representations to learn a sequence model using a combination of bidirectional LSTM with self-attention and CRF. Unlike typical dialog models that rely on huge, complex neural network architectures and large-scale pre-trained Transformers to achieve state-of-the-art results, our method achieves comparable results to BERT and even outperforms its smaller variant DistilBERT on conversational slot extraction tasks. Our method is faster than BERT models while achieving significant model size reduction–our model requires 135x and 81x fewer model parameters than BERT and DistilBERT, respectively. We conduct experiments on multiple conversational datasets and show significant improvements over existing methods including recent on-device models. Experimental results and ablation studies also show that our neural models preserve tiny memory footprint necessary to operate on smart devices, while still maintaining high performance.

In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems. We introduce a system consisting of iterative self-training and an extensible mini-template framework that textualizes the structured input data into semi-natural text to fully take advantage of pre-trained language models. We compare var-ious representations of NLG models’ input and output and show that transforming the input and output to be similar to what the language model has seen before during pre-training improves the model’s few-shot performance substantially. We show that neural mod-els can be trained with as few as 300 annotated examples while providing high fidelity, considerably lowering the resource requirements for standing up a new domain or language.This level of data efficiency removes the need for crowd-sourced data collection resulting in higher quality data annotated by expert linguists. In addition, model maintenance and debugging processes will improve in this few-shot setting. Finally, we explore distillation and using a caching system to satisfy latency requirements of real-world systems.

Human-assisting systems such as dialogue systems must take thoughtful, appropriate actions not only for clear and unambiguous user requests, but also for ambiguous user requests, even if the users themselves are not aware of their potential requirements. To construct such a dialogue agent, we collected a corpus and developed a model that classifies ambiguous user requests into corresponding system actions. In order to collect a high-quality corpus, we asked workers to input antecedent user requests whose pre-defined actions could be regarded as thoughtful. Although multiple actions could be identified as thoughtful for a single user request, annotating all combinations of user requests and system actions is impractical. For this reason, we fully annotated only the test data and left the annotation of the training data incomplete. In order to train the classification model on such training data, we applied the positive/unlabeled (PU) learning method, which assumes that only a part of the data is labeled with positive examples. The experimental results show that the PU learning method achieved better performance than the general positive/negative (PN) learning method to classify thoughtful actions given an ambiguous user request.

This paper proposes a taxonomy of errors in chat-oriented dialogue systems. Previously, two taxonomies were proposed; one is theory-driven and the other data-driven. The former suffers from the fact that dialogue theories for human conversation are often not appropriate for categorizing errors made by chat-oriented dialogue systems. The latter has limitations in that it can only cope with errors of systems for which we have data. This paper integrates these two taxonomies to create a comprehensive taxonomy of errors in chat-oriented dialogue systems. We found that, with our integrated taxonomy, errors can be reliably annotated with a higher Fleiss’ kappa compared with the previously proposed taxonomies.

Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot conversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy.

Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to generate their own labelled training data (given some seed data) and then train themselves from that data to perform a given task. We use reinforcement learning to optimize the data generation process where the reward signal is the agent’s performance on the task. The task can be any language-related task, from intent detection to full task-oriented conversations. In this work, we show that our approach is able to generalise from seed data and performs well in limited data and limited computation settings, with significant gains for intent detection and slot tagging across multiple datasets: ATIS, TOD, SNIPS, and Restaurants8k. We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data. We also conduct an analysis of the novelty of the generated data and provide generated examples for intent detection, slot tagging, and non-goal oriented conversations.

Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and CommonsenseQA) mainly focus on the discriminative task of choosing the right answer from a set of candidates, and do not involve interactive language generation as in dialogue. Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet. In this paper, we present an empirical study of commonsense in dialogue response generation. We first auto-extract commonsensical dialogues from existing dialogue datasets by leveraging ConceptNet, a commonsense knowledge graph. Furthermore, building on social contexts/situations in SocialIQA, we collect a new dialogue dataset with 25K dialogues aimed at exhibiting social commonsense in an interactive setting. We evaluate response generation models trained using these datasets and find that models trained on both extracted and our collected data produce responses that consistently exhibit more commonsense than baselines. Finally we propose an approach for automatic evaluation of commonsense that relies on features derived from ConceptNet and pre-trained language and dialog models, and show reasonable correlation with human evaluation of responses’ commonsense quality.

We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-oriented dialogues, especially for low-resourced emerging domains.

There is a growing interest in virtual assistants with multimodal capabilities, e.g., inferring the context of a conversation through scene understanding. The recently released situated and interactive multimodal conversations (SIMMC) dataset addresses this trend by enabling research to create virtual assistants, which are capable of taking into account the scene that user sees when conversing with the user and also interacting with items in the scene. The SIMMC dataset is novel in that it contains fully annotated user-assistant, task-orientated dialogs where the user and an assistant co-observe the same visual elements and the latter can take actions to update the scene. The SIMMC challenge, held as part of theNinth Dialog System Technology Challenge(DSTC9), propelled the development of various models which together set a new state-of-the-art on the SIMMC dataset. In this work, we compare and analyze these models to identify‘what worked?’, and the remaining gaps;‘whatnext?’. Our analysis shows that even though pretrained language models adapted to this set-ting show great promise, there are indications that multimodal context isn’t fully utilised, and there is a need for better and scalable knowledge base integration. We hope this first-of-its-kind analysis for SIMMC models provides useful insights and opportunities for further research in multimodal conversational agents

Sentence ordering is the task of arranging a given bag of sentences so as to maximise the coherence of the overall text. In this work, we propose a simple yet effective training method that improves the capacity of models to capture overall text coherence based on training over pairs of sentences/segments. Experimental results show the superiority of our proposed method in in- and cross-domain settings. The utility of our method is also verified over a multi-document summarisation task.

Topic diversion occurs frequently with engaging open-domain dialogue systems like virtual assistants. The balance between staying on topic and rectifying the topic drift is important for a good collaborative system. In this paper, we present a model which uses a fine-tuned XLNet-base to classify the utterances pertaining to the major topic of conversation and those which are not, with a precision of 84%. We propose a preliminary study, classifying utterances into major, minor and off-topics, which further extends into a system initiative for diversion rectification. A case study was conducted where a system initiative is emulated as a response to the user going off-topic, mimicking a common occurrence of mixed initiative present in natural human-human conversation. This task of classifying utterances into those which belong to the major theme or not, would also help us in identification of relevant sentences for tasks like dialogue summarization and information extraction from conversations.

Dialogue topic segmentation is critical in several dialogue modeling problems. However, popular unsupervised approaches only exploit surface features in assessing topical coherence among utterances. In this work, we address this limitation by leveraging supervisory signals from the utterance-pair coherence scoring task. First, we present a simple yet effective strategy to generate a training corpus for utterance-pair coherence scoring. Then, we train a BERT-based neural utterance-pair coherence model with the obtained training corpus. Finally, such model is used to measure the topical relevance between utterances, acting as the basis of the segmentation inference. Experiments on three public datasets in English and Chinese demonstrate that our proposal outperforms the state-of-the-art baselines.

To maintain utterance quality of a persona-aware dialog system, inappropriate utterances for the persona should be thoroughly filtered. When evaluating the appropriateness of a large number of arbitrary utterances to be registered in the utterance database of a retrieval-based dialog system, evaluation metrics that require a reference (or a “correct” utterance) for each evaluation target cannot be used. In addition, practical utterance filtering requires the ability to select utterances based on the intensity of persona characteristics. Therefore, we are developing metrics that can be used to capture the intensity of persona characteristics and can be computed without references tailored to the evaluation targets. To this end, we explore existing metrics and propose two new metrics: persona speaker probability and persona term salience. Experimental results show that our proposed metrics show weak to moderate correlations between scores of persona characteristics based on human judgments and outperform other metrics overall in filtering inappropriate utterances for particular personas.

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data’s probability distribution prevents the model from learning the one-to-many relations efficiently. Then we explore approaches to multi-referenced training in two aspects. Data-wise, we generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution. Model-wise, we propose to equip variational models with an expressive prior, named linear Gaussian model (LGM). Experimental results of automated evaluation and human evaluation show that the methods yield significant improvements over baselines.

Responses generated by neural conversational models (NCMs) for non-task-oriented systems are difficult to evaluate. We propose contrastive response pairs (CRPs) for automatically evaluating responses from non-task-oriented NCMs. We conducted an error analysis on responses generated by an encoder-decoder recurrent neural network (RNN) type NCM and created three types of CRPs corresponding to the three most frequent errors found in the analysis. Three NCMs of different response quality were objectively evaluated with the CRPs and compared to a subjective assessment. The correctness obtained by the three types of CRPs were consistent with the results of the subjective assessment.

Natural conversations are filled with disfluencies. This study investigates if and how BERT understands disfluency with three experiments: (1) a behavioural study using a downstream task, (2) an analysis of sentence embeddings and (3) an analysis of the attention mechanism on disfluency. The behavioural study shows that without fine-tuning on disfluent data, BERT does not suffer significant performance loss when presented disfluent compared to fluent inputs (exp1). Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2). This indicates that deep layers of BERT become relatively invariant to disfluency. We pinpoint attention as a potential mechanism that could explain this phenomenon (exp3). Overall, the study suggests that BERT has knowledge of disfluency structure. We emphasise the potential of using BERT to understand natural utterances without disfluency removal.

Dialogue State Tracking (DST) is a sub-task of task-based dialogue systems where the user intention is tracked through a set of (domain, slot, slot-value) triplets. Existing DST models can be difficult to extend for new datasets with larger domains/slots mainly due to either of the two reasons- i) prediction of domain-slot as a pair, and ii) dependency of model parameters on the number of slots and domains. In this work, we propose to address these issues using a Hierarchical DST (Hi-DST) model. At a given turn, the model first detects a change in domain followed by domain prediction if required. Then it decides suitable action for each slot in the predicted domains and finds their value accordingly. The model parameters of Hi-DST are independent of the number of domains/slots. Due to the hierarchical modeling, it achieves O(|M|+|N|) belief state prediction for a single turn where M and N are the set of unique domains and slots respectively. We argue that the hierarchical structure helps in the model explainability and makes it easily extensible to new datasets. Experiments on the MultiWOZ dataset show that our proposed model achieves comparable joint accuracy performance to state-of-the-art DST models.

Most recently proposed approaches in dialogue state tracking (DST) leverage the context and the last dialogue states to track current dialogue states, which are often slot-value pairs. Although the context contains the complete dialogue information, the information is usually indirect and even requires reasoning to obtain. The information in the lastly predicted dialogue states is direct, but when there is a prediction error, the dialogue information from this source will be incomplete or erroneous. In this paper, we propose the Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations network (FPDSC). This model extracts information of each dialogue turn by modeling interactions among each turn utterance, the corresponding last dialogue states, and dialogue slots. Then the representation of each dialogue turn is aggregated by a hierarchical structure to form the passage information, which is utilized in the current turn of DST. Experimental results validate the effectiveness of the fusion network with 55.03% and 59.07% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, which reaches the state-of-the-art performance. Furthermore, we conduct the deleted-value and related-slot experiments on MultiWOZ 2.1 to evaluate our model.

This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metrics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model’s ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.

We present Scikit-talk, an open-source toolkit for processing collections of real-world conversational speech in Python. First of its kind, the toolkit equips those interested in studying or modeling conversations with an easy-to-use interface to build and explore large collections of transcriptions and annotations of talk-in-interaction. Designed for applications in speech processing and Conversational AI, Scikit-talk provides tools to custom-build datasets for tasks such as intent prototyping, dialog flow testing, and conversation design. Its preprocessor module comes with several pre-built interfaces for common transcription formats, which aim to make working across multiple data sources more accessible. The explorer module provides a collection of tools to explore and analyse this data type via string matching and unsupervised machine learning techniques. Scikit-talk serves as a platform to collect and connect different transcription formats and representations of talk, enabling the user to quickly build multilingual datasets of varying detail and granularity. Thus, the toolkit aims to make working with authentic conversational speech data in Python more accessible and to provide the user with comprehensive options to work with representations of talk in appropriate detail for any downstream task. For the latest updates and information on currently supported languages and language resources, please refer to: https://pypi.org/project/scikit-talk/

Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface: a web-based virtual agent, Nora vs. the android ERICA via a video call. The experimental results show that the android can offer a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.

We demonstrate the moderating abilities of a multi-party attentive listening robot system when multiple people are speaking in turns. Our conventional one-on-one attentive listening system generates listener responses such as backchannels, repeats, elaborating questions, and assessments. In this paper, additional robot responses that stimulate a listening user (side participant) to become more involved in the dialogue are proposed. The additional responses elicit assessments and questions from the side participant, making the dialogue more empathetic and lively.

Time-offset interaction applications (TOIA) allow simulating conversations with people who have previously recorded relevant video utterances, which are played in response to their interacting user. TOIAs have great potential for preserving cross-generational and cross-cultural histories, online teaching, simulated interviews, etc. Current TOIAs exist in niche contexts involving high production costs. Democratizing TOIA presents different challenges when creating appropriate pre-recordings, designing different user stories, and creating simple online interfaces for experimentation. We open-source TOIA 2.0, a user-centered time-offset interaction application, and make it available for everyone who wants to interact with people’s pre-recordings, or create their pre-recordings.

This paper explores character-driven story continuation, in which the story emerges through characters’ first- and second-person narration as well as dialogue—requiring models to select language that is consistent with a character’s persona and their relationships with other characters while following and advancing the story. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. To this end, we extend the Critical Role Dungeons and Dragons Dataset (Rameshkumar and Bailey, 2020)—consisting of dialogue transcripts of people collaboratively telling a story while playing the role-playing game Dungeons and Dragons—with automatically extracted relationships between each pair of interacting characters as well as their personas. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.

Regular physical activity is associated with a reduced risk of chronic diseases such as type 2 diabetes and improved mental well-being. Yet, more than half of the US population is insufficiently active. Health coaching has been successful in promoting healthy behaviors. In this paper, we present our work towards assisting health coaches by extracting the physical activity goal the user and coach negotiate via text messages. We show that information captured by dialogue acts can help to improve the goal extraction results. We employ both traditional and transformer-based machine learning models for dialogue acts prediction and find them statistically indistinguishable in performance on our health coaching dataset. Moreover, we discuss the feedback provided by the health coaches when evaluating the correctness of the extracted goal summaries. This work is a step towards building a virtual assistant health coach to promote a healthy lifestyle.

Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD

Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing. Explaining human conversations poses a great challenge as it requires contextual understanding, planning, inference, and several aspects of reasoning including causal, temporal, and commonsense reasoning. In this work, we introduce CIDER – a manually curated dataset that contains dyadic dialogue explanations in the form of implicit and explicit knowledge triplets inferred using contextual commonsense inference. Extracting such rich explanations from conversations can be conducive to improving several downstream applications. The annotated triplets are categorized by the type of commonsense knowledge present (e.g., causal, conditional, temporal). We set up three different tasks conditioned on the annotated dataset: Dialogue-level Natural Language Inference, Span Extraction, and Multi-choice Span Selection. Baseline results obtained with transformer-based models reveal that the tasks are difficult, paving the way for promising future research. The dataset and the baseline implementations are publicly available at https://github.com/declare-lab/CIDER.

Discourse parsers recognize the intentional and inferential relationships that organize extended texts. They have had a great influence on a variety of NLP tasks as well as theoretical studies in linguistics and cognitive science. However it is often difficult to achieve good results from current discourse models, largely due to the difficulty of the task, particularly recognizing implicit discourse relations. Recent developments in transformer-based models have shown great promise on these analyses, but challenges still remain. We present a position paper which provides a systematic analysis of the state of the art discourse parsers. We aim to examine the performance of current discourse parsing models via gradual domain shift: within the corpus, on in-domain texts, and on out-of-domain texts, and discuss the differences between the transformer-based models and the previous models in predicting different types of implicit relations both inter- and intra-sentential. We conclude by describing several shortcomings of the existing models and a discussion of how future work should approach this problem.

MultiWOZ (Budzianowski et al., 2018) is one of the most popular multi-domain taskoriented dialog datasets, containing 10K+ annotated dialogs covering eight domains. It has been widely accepted as a benchmark for various dialog tasks, e.g., dialog state tracking (DST), natural language generation (NLG) and end-to-end (E2E) dialog modeling. In this work, we identify an overlooked issue with dialog state annotation inconsistencies in the dataset, where a slot type is tagged inconsistently across similar dialogs leading to confusion for DST modeling. We propose an automated correction for this issue, which is present in 70% of the dialogs. Additionally, we notice that there is significant entity bias in the dataset (e.g., “cambridge” appears in 50% of the destination cities in the train domain). The entity bias can potentially lead to named entity memorization in generative models, which may go unnoticed as the test set suffers from a similar entity bias as well. We release a new test set with all entities replaced with unseen entities. Finally, we benchmark joint goal accuracy (JGA) of the state-of-theart DST baselines on these modified versions of the data. Our experiments show that the annotation inconsistency corrections lead to 7-10% improvement in JGA. On the other hand, we observe a 29% drop in JGA when models are evaluated on the new test set with unseen entities.

We present a comprehensive survey of available corpora for multi-party dialogue. We survey over 300 publications related to multi-party dialogue and catalogue all available corpora in a novel taxonomy. We analyze methods of data collection for multi-party dialogue corpora and identify several lacunae in existing data collection approaches used to collect such dialogue. We present this survey, the first survey to focus exclusively on multi-party dialogue corpora, to motivate research in this area. Through our discussion of existing data collection methods, we identify desiderata and guiding principles for multi-party data collection to contribute further towards advancing this area of dialogue research.

Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world. To better understand this problem, we need data about asking questions in situated task-based interactions. To this end, we present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment in which human participants play the role of a robot performing a collaborative tool-organization task. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment. We provide the corpus as an empirically-grounded resource for improving question generation in situated intelligent agents.

Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles. Tools which can present strong arguments relevant to oneself could help to reduce those problems. To be able to evaluate algorithms which can predict how convincing an argument is, we have collected a dataset with more than 900 arguments and personal attitudes of 600 individuals, which we present in this paper. Based on this data, we suggest three recommender tasks, for which we provide two baseline results from a simple majority classifier and a more complex nearest-neighbor algorithm. Our results suggest that better algorithms can still be developed, and we invite the community to improve on our results.

Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are inflexible regarding the topics they can discuss. To address this issue, we propose a combination of argumentative dialogue systems with argument search technology that enables a system to discuss any topic on which the search engine is able to find suitable arguments. Our approach utilizes supervised learning-based relation classification to map the retrieved arguments into a general tree structure for use in dialogue systems. We evaluate the approach with a state of the art search engine and a recently introduced dialogue model in an extensive user study with respect to the dialogue coherence. The results vary between the investigated topics (and hence depend on the quality of the underlying data) but are in some instances surprisingly close to the results achieved with a manually annotated argument structure.

Most existing methods for automatic fact-checking start with a precompiled list of claims to verify. We investigate the understudied problem of determining what statements in news articles are worthy to fact-check. We annotate the argument structure of 95 news articles in the climate change domain that are fact-checked by climate scientists at climatefeedback.org. We release the first multi-layer annotated corpus for both argumentative discourse structure (argument types and relations) and for fact-checked statements in news articles. We discuss the connection between argument structure and check-worthy statements and develop several baseline models for detecting check-worthy statements in the climate change domain. Our preliminary results show that using information about argumentative discourse structure shows slight but statistically significant improvement over a baseline of local discourse structure.

Open-domain chatbots are supposed to converse freely with humans without being restricted to a topic, task or domain. However, the boundaries and/or contents of open-domain conversations are not clear. To clarify the boundaries of “openness”, we conduct two studies: First, we classify the types of “speech events” encountered in a chatbot evaluation data set (i.e., Meena by Google) and find that these conversations mainly cover the “small talk” category and exclude the other speech event categories encountered in real life human-human communication. Second, we conduct a small-scale pilot study to generate online conversations covering a wider range of speech event categories between two humans vs. a human and a state-of-the-art chatbot (i.e., Blender by Facebook). A human evaluation of these generated conversations indicates a preference for human-human conversations, since the human-chatbot conversations lack coherence in most speech event categories. Based on these results, we suggest (a) using the term “small talk” instead of “open-domain” for the current chatbots which are not that “open” in terms of conversational abilities yet, and (b) revising the evaluation methods to test the chatbot conversations against other speech events.

Recently, principal reward components for dialogue policy reinforcement learning use task success and user satisfaction independently and neither the resulting learned behaviour has been analysed nor a suitable proper analysis method even existed. In this work, we employ both principal reward components jointly and propose a method to analyse the resulting behaviour through a structured way of probing the learned policy. We show that blending both reward components increases user satisfaction without sacrificing task success in more hostile environments and provide insight about actions chosen by the learned policies.

The ability for variation in language use is necessary for speakers to achieve their conversational goals, for instance when referring to objects in visual environments. We argue that diversity should not be modelled as an independent objective in dialogue, but should rather be a result or by-product of goal-oriented language generation. Different lines of work in neural language generation investigated decoding methods for generating more diverse utterances, or increasing the informativity through pragmatic reasoning. We connect those lines of work and analyze how pragmatic reasoning during decoding affects the diversity of generated image captions. We find that boosting diversity itself does not result in more pragmatically informative captions, but pragmatic reasoning does increase lexical diversity. Finally, we discuss whether the gain in informativity is achieved in linguistically plausible ways.

Automated Frequently Asked Question (FAQ) retrieval provides an effective procedure to provide prompt responses to natural language based queries, providing an efficient platform for large-scale service-providing companies for presenting readily available information pertaining to customers’ questions. We propose DTAFA, a novel multi-lingual FAQ retrieval system that aims at improving the top-1 retrieval accuracy with the least number of parameters. We propose two decoupled deep learning architectures trained for (i) candidate generation via text classification for a user question, and (ii) learning fine-grained semantic similarity between user questions and the FAQ repository for candidate refinement. We validate our system using real-life enterprise data as well as open source dataset. Empirically we show that DTAFA achieves better accuracy compared to existing state-of-the-art while requiring nearly 30× lesser number of training parameters.

The ability to take turns in a fluent way (i.e., without long response delays or frequent interruptions) is a fundamental aspect of any spoken dialog system. However, practical speech recognition services typically induce a long response delay, as it takes time before the processing of the user’s utterance is complete. There is a considerable amount of research indicating that humans achieve fast response times by projecting what the interlocutor will say and estimating upcoming turn completions. In this work, we implement this mechanism in an incremental spoken dialog system, by using a language model that generates possible futures to project upcoming completion points. In theory, this could make the system more responsive, while still having access to semantic information not yet processed by the speech recognizer. We conduct a small study which indicates that this is a viable approach for practical dialog systems, and that this is a promising direction for future research.

Recently, transformer language models have been applied to build both task- and non-task-oriented dialogue systems. Although transformers perform well on most of the NLP tasks, they perform poorly on context retrieval and symbolic reasoning. Our work aims to address this limitation by embedding the model in an operational loop that blends both natural language generation and symbolic injection. We evaluated our system on the multi-domain DSTC8 data set and reported joint goal accuracy of 75.8% (ranked among the first half positions), intent accuracy of 97.4% (which is higher than the reported literature), and a 15% improvement for success rate compared to a baseline with no symbolic injection. These promising results suggest that transformer language models can not only generate proper system responses but also symbolic representations that can further be used to enhance the overall quality of the dialogue management as well as serving as scaffolding for complex conversational reasoning.

Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator instead of humans. These user simulators have their own problems. While hand-coded, rule-based user simulators have been shown to be sufficient in small, simple domains, for complex domains the number of rules quickly becomes intractable. State-of-the-art data-driven user simulators, on the other hand, are still domain-dependent. This means that adaptation to each new domain requires redesigning and retraining. In this work, we propose a domain-independent transformer-based user simulator (TUS). The structure of TUS is not tied to a specific domain, enabling domain generalization and the learning of cross-domain user behaviour from data. We compare TUS with the state-of-the-art using automatic as well as human evaluations. TUS can compete with rule-based user simulators on pre-defined domains and is able to generalize to unseen domains in a zero-shot fashion.

Live chat in customer service platforms is critical for serving clients online. For multi-turn question-answering live chat, typical Question Answering systems are single-turn and focus on factoid questions; alternatively, modeling as goal-oriented dialogue limits us to narrower domains. Motivated by these challenges, we develop a new approach based on a framework from a different discipline: Community Question Answering. Specifically, we opt to divide and conquer the task into two sub-tasks: (1) Question-Question Similarity, where we gain more than 9% absolute improvement in F1 over baseline; and (2) Answer Utterances Extraction, where we achieve a high F1 score of 87% for this new sub-task. Further, our user engagement metrics reveal how the enterprise support representatives benefit from the 2-step approach we deployed to production.

Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (NLL) or cross-entropy. Such commonly used training objectives do not foster generating alternate responses to a context. But, the effects of minimizing an alternate training objective that fosters a model to generate alternate response and score it on semantic similarity has not been well studied. We hypothesize that a language generation model can improve on its diversity by learning to generate alternate text during training and minimizing a semantic loss as an auxiliary objective. We explore this idea on two different sized data sets on the task of next utterance generation in goal oriented dialogues. We make two observations (1) minimizing a semantic objective improved diversity in responses in the smaller data set (Frames) but only as-good-as minimizing the NLL in the larger data set (MultiWoZ) (2) large language model embeddings can be more useful as a semantic loss objective than as initialization for token embeddings.

Predicting the next utterance in dialogue is contingent on encoding of users’ input text to generate appropriate and relevant response in data-driven approaches. Although the semantic and syntactic quality of the language generated is evaluated, more often than not, the encoded representation of input is not evaluated. As the representation of the encoder is essential for predicting the appropriate response, evaluation of encoder representation is a challenging yet important problem. In this work, we showcase evaluating the text generated through human or automatic metrics is not sufficient to appropriately evaluate soundness of the language understanding of dialogue models and, to that end, propose a set of probe tasks to evaluate encoder representation of different language encoders commonly used in dialogue models. From experiments, we observe that some of the probe tasks are easier and some are harder for even sophisticated model architectures to learn. And, through experiments we observe that RNN based architectures have lower performance on automatic metrics on text generation than transformer model but perform better than the transformer model on the probe tasks indicating that RNNs might preserve task information better than the Transformers.

In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm. We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning. We present GenSF (Generative Slot Filling), which leverages a generative pre-trained open-domain dialog model for slot filling. GenSF (1) adapts the pre-trained model by incorporating inductive biases about the task and (2) adapts the downstream task by reformulating slot filling to better leverage the pre-trained model’s capabilities. GenSF achieves state-of-the-art results on two slot filling datasets with strong gains in few-shot and zero-shot settings. We achieve a 9 F1 score improvement in zero-shot slot filling. This highlights the value of strong alignment between the pre-trained model and the downstream task.

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors and associating accurate status/actions with the corresponding interlocutors and person mentions.

Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information. Training traditional extractive summarization models relies heavily on human-engineered labels such as sentence-level annotations of summary-worthiness. However, in many use cases, such human-engineered labels do not exist and manually annotating thousands of documents for the purpose of training models may not be feasible. On the other hand, indirect signals for summarization are often available, such as agent actions for customer service dialogues, headlines for news articles, diagnosis for Electronic Health Records, etc. In this paper, we develop a general framework that generates extractive summarization as a byproduct of supervised learning tasks for indirect signals via the help of attention mechanism. We test our models on customer service dialogues and experimental results demonstrated that our models can reliably select informative sentences and words for automatic summarization.

In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue. We use crowd-sourcing paradigm with a model-in-loop approach for collecting the summaries and compare the data with the expert summaries. We leverage the question generation paradigm to automatically generate questions from the dialogue, which can be used to validate the user participation and potentially also draw attention of the user towards the contents then need to summarize. We then develop several models for abstractive summary generation in the Incremental temporal scenario. We perform a detailed analysis of the results and show that including the past context into the summary generation yields better summaries.

This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.

Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior way is integral for these chatbots. Currently, the main approach to handling political sensitivity is by simply changing such a topic when it is detected. This is safe but evasive and results in a chatbot that is less engaging. In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence. We then conduct political prudence analysis of various chatbots and discuss their behavior from multiple angles through our automatic metric and human evaluation metrics. The testsets and codebase are released to promote research in this area.

As voice assistants and dialogue agents grow in popularity, so does the abuse they receive. We conducted a large-scale quantitative evaluation of the effectiveness of 4 response types (avoidance, why, empathetic, and counter), and 2 additional factors (using a redirect or a voluntarily provided name) that have not been tested by prior work. We measured their direct effectiveness on real users in-the-wild by the re-offense ratio, length of conversation after the initial response, and number of turns until the next re-offense. Our experiments confirm prior lab studies in showing that empathetic responses perform better than generic avoidance responses as well as counter responses. We show that dialogue agents should almost always guide offensive users to a new topic through the use of redirects and use the user’s name if provided. As compared to a baseline avoidance strategy employed by commercial agents, our best strategy is able to reduce the re-offense ratio from 92% to 43%.