Milica Gasic

Also published as: Milica Gašić


2022

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GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers
Hsien-chin Lin | Christian Geishauser | Shutong Feng | Nurul Lubis | Carel van Niekerk | Michael Heck | Milica Gasic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

User simulators (USs) are commonly used to train task-oriented dialogue systems via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. Incorporating a natural language generation (NLG) module with USs during training can partly deal with this problem. However, since the policy and NLG of USs are optimised separately, these simulated user utterances may not be natural enough in a given context. In this work, we propose a generative transformer-based user simulator (GenTUS). GenTUS consists of an encoder-decoder structure, which means it can optimise both the user policy and natural language generation jointly. GenTUS generates both semantic actions and natural language utterances, preserving interpretability and enhancing language variation. In addition, by representing the inputs and outputs as word sequences and by using a large pre-trained language model we can achieve generalisability in feature representation. We evaluate GenTUS with automatic metrics and human evaluation. Our results show that GenTUS generates more natural language and is able to transfer to an unseen ontology in a zero-shot fashion. In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators.

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Dialogue Evaluation with Offline Reinforcement Learning
Nurul Lubis | Christian Geishauser | Hsien-chin Lin | Carel van Niekerk | Michael Heck | Shutong Feng | Milica Gasic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Task-oriented dialogue systems aim to fulfill user goals through natural language interactions. They are ideally evaluated with human users, which however is unattainable to do at every iteration of the development phase. Simulated users could be an alternative, however their development is nontrivial. Therefore, researchers resort to offline metrics on existing human-human corpora, which are more practical and easily reproducible. They are unfortunately limited in reflecting real performance of dialogue systems. BLEU for instance is poorly correlated with human judgment, and existing corpus-based metrics such as success rate overlook dialogue context mismatches. There is still a need for a reliable metric for task-oriented systems with good generalization and strong correlation with human judgements. In this paper, we propose the use of offline reinforcement learning for dialogue evaluation based on static data. Such an evaluator is typically called a critic and utilized for policy optimization. We go one step further and show that offline RL critics can be trained for any dialogue system as external evaluators, allowing dialogue performance comparisons across various types of systems. This approach has the benefit of being corpus- and model-independent, while attaining strong correlation with human judgements, which we confirm via an interactive user trial.

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Dialogue Term Extraction using Transfer Learning and Topological Data Analysis
Renato Vukovic | Michael Heck | Benjamin Ruppik | Carel van Niekerk | Marcus Zibrowius | Milica Gasic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots and values. As we move towards adaptable dialogue systems where knowledge about domains, slots and values may change, there is an increasing need to automatically extract these terms from raw dialogues or related non-dialogue data on a large scale. In this paper, we take an important step in this direction by exploring different features that can enable systems to discover realisations of domains, slots and values in dialogues in a purely data-driven fashion. The features that we examine stem from word embeddings, language modelling features, as well as topological features of the word embedding space. To examine the utility of each feature set, we train a seed model based on the widely used MultiWOZ data-set. Then, we apply this model to a different corpus, the Schema-guided dialogue data-set. Our method outperforms the previously proposed approach that relies solely on word embeddings. We also demonstrate that each of the features is responsible for discovering different kinds of content. We believe our results warrant further research towards ontology induction, and continued harnessing of topological data analysis for dialogue and natural language processing research.

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EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
Shutong Feng | Nurul Lubis | Christian Geishauser | Hsien-chin Lin | Michael Heck | Carel van Niekerk | Milica Gasic
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.

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Robust Dialogue State Tracking with Weak Supervision and Sparse Data
Michael Heck | Nurul Lubis | Carel van Niekerk | Shutong Feng | Christian Geishauser | Hsien-Chin Lin | Milica Gašić
Transactions of the Association for Computational Linguistics, Volume 10

Generalizing dialogue state tracking (DST) to new data is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. Sample sparsity, distributional shift, and the occurrence of new concepts and topics frequently lead to severe performance degradation during inference. In this paper we propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. Two novel input-level dropout methods mitigate the negative impact of sample sparsity. We propose a new model architecture with a unified encoder that supports value as well as slot independence by leveraging the attention mechanism. We combine the strengths of triple copy strategy DST and value matching to benefit from complementary predictions without violating the principle of ontology independence. Our experiments demonstrate that an extractive DST model can be trained without manual span labels. Our architecture and training strategies improve robustness towards sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. We further highlight our model’s ability to effectively learn from non-dialogue data.

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Dynamic Dialogue Policy for Continual Reinforcement Learning
Christian Geishauser | Carel van Niekerk | Hsien-chin Lin | Nurul Lubis | Michael Heck | Shutong Feng | Milica Gašić
Proceedings of the 29th International Conference on Computational Linguistics

Continual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented dialogue system must have the capability to continually learn, dynamically adapting to new challenges while preserving the knowledge it already acquired. Despite the importance, continual reinforcement learning of the dialogue policy has remained largely unaddressed. The lack of a framework with training protocols, baseline models and suitable metrics, has so far hindered research in this direction. In this work we fill precisely this gap, enabling research in dialogue policy optimisation to go from static to dynamic learning. We provide a continual learning algorithm, baseline architectures and metrics for assessing continual learning models. Moreover, we propose the dynamic dialogue policy transformer (DDPT), a novel dynamic architecture that can integrate new knowledge seamlessly, is capable of handling large state spaces and obtains significant zero-shot performance when being exposed to unseen domains, without any growth in network parameter size. We validate the strengths of DDPT in simulation with two user simulators as well as with humans.

2021

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Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance
Carel van Niekerk | Andrey Malinin | Christian Geishauser | Michael Heck | Hsien-chin Lin | Nurul Lubis | Shutong Feng | Milica Gasic
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.

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Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems
Hsien-chin Lin | Nurul Lubis | Songbo Hu | Carel van Niekerk | Christian Geishauser | Michael Heck | Shutong Feng | Milica Gasic
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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.

2020

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Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems
Yen-chen Wu | Bo-Hsiang Tseng | Milica Gasic
Findings of the Association for Computational Linguistics: EMNLP 2020

In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). Although I2A achieves a higher success rate than baselines by augmenting predicted future into a policy network, its complicated architecture introduces unwanted instability. In this work, we propose actor-double-critic (ADC) to improve the stability and overall performance of I2A. ADC simplifies the architecture of I2A to reduce excessive parameters and hyper-parameters. More importantly, a separate model-based critic shares parameters between actions and makes back-propagation explicit. In our experiments on Cambridge Restaurant Booking task, ADC enhances success rates considerably and shows robustness to imperfect environment models. In addition, ADC exhibits the stability and sample-efficiency as significantly reducing the baseline standard deviation of success rates and reaching the 80% success rate with half training data.

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Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
Carel van Niekerk | Michael Heck | Christian Geishauser | Hsien-chin Lin | Nurul Lubis | Marco Moresi | Milica Gasic
Findings of the Association for Computational Linguistics: EMNLP 2020

The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state trackers achieve just over 55% accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state. Belief trackers, on the other hand, maintain a distribution over possible dialogue states. However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions. In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models. Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.

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TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
Michael Heck | Carel van Niekerk | Nurul Lubis | Christian Geishauser | Hsien-Chin Lin | Marco Moresi | Milica Gasic
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user’s goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system’s inform operations (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.

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LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization
Nurul Lubis | Christian Geishauser | Michael Heck | Hsien-chin Lin | Marco Moresi | Carel van Niekerk | Milica Gasic
Proceedings of the 28th International Conference on Computational Linguistics

Reinforcement learning (RL) can enable task-oriented dialogue systems to steer the conversation towards successful task completion. In an end-to-end setting, a response can be constructed in a word-level sequential decision making process with the entire system vocabulary as action space. Policies trained in such a fashion do not require expert-defined action spaces, but they have to deal with large action spaces and long trajectories, making RL impractical. Using the latent space of a variational model as action space alleviates this problem. However, current approaches use an uninformed prior for training and optimize the latent distribution solely on the context. It is therefore unclear whether the latent representation truly encodes the characteristics of different actions. In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning. We choose response auto-encoding as the auxiliary task, as this captures the generative factors of dialogue responses while requiring low computational cost and neither additional data nor labels. Our approach yields a more action-characterized latent representations which support end-to-end dialogue policy optimization and achieves state-of-the-art success rates. These results warrant a more wide-spread use of RL in end-to-end dialogue models.

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Out-of-Task Training for Dialog State Tracking Models
Michael Heck | Christian Geishauser | Hsien-chin Lin | Nurul Lubis | Marco Moresi | Carel van Niekerk | Milica Gasic
Proceedings of the 28th International Conference on Computational Linguistics

Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.

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Topology of Word Embeddings: Singularities Reflect Polysemy
Alexander Jakubowski | Milica Gasic | Marcus Zibrowius
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

The manifold hypothesis suggests that word vectors live on a submanifold within their ambient vector space. We argue that we should, more accurately, expect them to live on a <i>pinched</i> manifold: a singular quotient of a manifold obtained by identifying some of its points. The identified, singular points correspond to polysemous words, i.e. words with multiple meanings. Our point of view suggests that monosemous and polysemous words can be distinguished based on the topology of their neighbourhoods. We present two kinds of empirical evidence to support this point of view: (1) We introduce a topological measure of polysemy based on persistent homology that correlates well with the actual number of meanings of a word. (2) We propose a simple, topologically motivated solution to the SemEval-2010 task on <i>Word Sense Induction & Disambiguation</i> that produces competitive results.

2019

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Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Satoshi Nakamura | Milica Gasic | Ingrid Zuckerman | Gabriel Skantze | Mikio Nakano | Alexandros Papangelis | Stefan Ultes | Koichiro Yoshino
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation
Bo-Hsiang Tseng | Paweł Budzianowski | Yen-chen Wu | Milica Gasic
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain. This is particularly the case for dialogue systems, where we want to be able to seamlessly include new domains into the conversation. Therefore, it is crucial for generation models to share knowledge across domains for the effective adaptation from one domain to another. In this study, we exploit a tree-structured semantic encoder to capture the internal structure of complex semantic representations required for multi-domain dialogues in order to facilitate knowledge sharing across domains. In addition, a layer-wise attention mechanism between the tree encoder and the decoder is adopted to further improve the model’s capability. The automatic evaluation results show that our model outperforms previous methods in terms of the BLEU score and the slot error rate, in particular when the adaptation data is limited. In subjective evaluation, human judges tend to prefer the sentences generated by our model, rating them more highly on informativeness and naturalness than other systems.

2018

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MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Paweł Budzianowski | Tsung-Hsien Wen | Bo-Hsiang Tseng | Iñigo Casanueva | Stefan Ultes | Osman Ramadan | Milica Gašić
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora.The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators;secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.

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Feudal Reinforcement Learning for Dialogue Management in Large Domains
Iñigo Casanueva | Paweł Budzianowski | Pei-Hao Su | Stefan Ultes | Lina M. Rojas-Barahona | Bo-Hsiang Tseng | Milica Gašić
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of dimensionality. We propose a novel Dialogue Management architecture, based on Feudal RL, which decomposes the decision into two steps; a first step where a master policy selects a subset of primitive actions, and a second step where a primitive action is chosen from the selected subset. The structural information included in the domain ontology is used to abstract the dialogue state space, taking the decisions at each step using different parts of the abstracted state. This, combined with an information sharing mechanism between slots, increases the scalability to large domains. We show that an implementation of this approach, based on Deep-Q Networks, significantly outperforms previous state of the art in several dialogue domains and environments, without the need of any additional reward signal.

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Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems
Florian Kreyssig | Iñigo Casanueva | Paweł Budzianowski | Milica Gašić
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.

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Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Stefan Ultes | Paweł Budzianowski | Iñigo Casanueva | Lina M. Rojas-Barahona | Bo-Hsiang Tseng | Yen-Chen Wu | Steve Young | Milica Gašić
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multiple entities of the same type. We demonstrate in a prototype implementation benefits of relation modelling on the dialogue level and show that a trained policy using these relations outperforms the multi-domain baseline. Furthermore, we show that by modelling the relations on the dialogue level, the system is capable of processing relations present in the user input and even learns to address them in the system response.

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Feudal Dialogue Management with Jointly Learned Feature Extractors
Iñigo Casanueva | Paweł Budzianowski | Stefan Ultes | Florian Kreyssig | Bo-Hsiang Tseng | Yen-chen Wu | Milica Gašić
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.

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Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Bo-Hsiang Tseng | Florian Kreyssig | Paweł Budzianowski | Iñigo Casanueva | Yen-Chen Wu | Stefan Ultes | Milica Gašić
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.

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Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Lina M. Rojas-Barahona | Bo-Hsiang Tseng | Yinpei Dai | Clare Mansfield | Osman Ramadan | Stefan Ultes | Michael Crawford | Milica Gašić
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.

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Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
Osman Ramadan | Paweł Budzianowski | Milica Gašić
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.

2017

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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić | Ivan Vulić | Diarmuid Ó Séaghdha | Ira Leviant | Roi Reichart | Milica Gašić | Anna Korhonen | Steve Young
Transactions of the Association for Computational Linguistics, Volume 5

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

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Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Stefan Ultes | Paweł Budzianowski | Iñigo Casanueva | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | Tsung-Hsien Wen | Milica Gašić | Steve Young
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.

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Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
Paweł Budzianowski | Stefan Ultes | Pei-Hao Su | Nikola Mrkšić | Tsung-Hsien Wen | Iñigo Casanueva | Lina M. Rojas-Barahona | Milica Gašić
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcement learning methods do not provide an efficient framework for modelling such dialogues. In this paper, we focus on the under-explored problem of multi-domain dialogue management. First, we propose a new method for hierarchical reinforcement learning using the option framework. Next, we show that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do. Moreover, we show how pretrained policies can be adapted to more complex systems with an additional set of new actions. In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.

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Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Pei-Hao Su | Paweł Budzianowski | Stefan Ultes | Milica Gašić | Steve Young
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.

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DialPort, Gone Live: An Update After A Year of Development
Kyusong Lee | Tiancheng Zhao | Yulun Du | Edward Cai | Allen Lu | Eli Pincus | David Traum | Stefan Ultes | Lina M. Rojas-Barahona | Milica Gasic | Steve Young | Maxine Eskenazi
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.

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PyDial: A Multi-domain Statistical Dialogue System Toolkit
Stefan Ultes | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Dongho Kim | Iñigo Casanueva | Paweł Budzianowski | Nikola Mrkšić | Tsung-Hsien Wen | Milica Gašić | Steve Young
Proceedings of ACL 2017, System Demonstrations

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A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen | David Vandyke | Nikola Mrkšić | Milica Gašić | Lina M. Rojas-Barahona | Pei-Hao Su | Stefan Ultes | Steve Young
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

2016

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Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | Stefan Ultes | David Vandyke | Steve Young
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
Pei-Hao Su | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Stefan Ultes | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Counter-fitting Word Vectors to Linguistic Constraints
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
Lina M. Rojas-Barahona | Milica Gašić | Nikola Mrkšić | Pei-Hao Su | Stefan Ultes | Tsung-Hsien Wen | Steve Young
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).

2015

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Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen | Milica Gašić | Nikola Mrkšić | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Multi-domain Dialog State Tracking using Recurrent Neural Networks
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
Tsung-Hsien Wen | Milica Gašić | Dongho Kim | Nikola Mrkšić | Pei-Hao Su | David Vandyke | Steve Young
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management
Lu Chen | Pei-Hao Su | Milica Gašić
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Pei-Hao Su | David Vandyke | Milica Gašić | Nikola Mrkšić | Tsung-Hsien Wen | Steve Young
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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The PARLANCE mobile application for interactive search in English and Mandarin
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Hugues Bouchard | Catherine Breslin | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gašić | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Tim Potter | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay | Boris Villazon-Terrazas | Majid Yazdani | Steve Young | Yanchao Yu
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

2013

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Demonstration of the PARLANCE system: a data-driven incremental, spoken dialogue system for interactive search
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gasic | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay
Proceedings of the SIGDIAL 2013 Conference

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POMDP-based dialogue manager adaptation to extended domains
Milica Gašić | Catherine Breslin | Matthew Henderson | Dongho Kim | Martin Szummer | Blaise Thomson | Pirros Tsiakoulis | Steve Young
Proceedings of the SIGDIAL 2013 Conference

2012

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The Effect of Cognitive Load on a Statistical Dialogue System
Milica Gašić | Pirros Tsiakoulis | Matthew Henderson | Blaise Thomson | Kai Yu | Eli Tzirkel | Steve Young
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2010

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Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning
François Mairesse | Milica Gašić | Filip Jurčíček | Simon Keizer | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Parameter estimation for agenda-based user simulation
Simon Keizer | Milica Gašić | Filip Jurčíček | François Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2010 Conference

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Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers
Milica Gašić | Filip Jurčíček | Simon Keizer | Francois Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2010 Conference

2009

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k-Nearest Neighbor Monte-Carlo Control Algorithm for POMDP-Based Dialogue Systems
Fabrice Lefèvre | Milica Gašić | Filip Jurčíček | Simon Keizer | François Mairesse | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the SIGDIAL 2009 Conference

2008

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Training and Evaluation of the HIS POMDP Dialogue System in Noise
Milica Gašić | Simon Keizer | Francois Mairesse | Jost Schatzmann | Blaise Thomson | Kai Yu | Steve Young
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue