Georgios Spithourakis


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Proceedings of the 4th Workshop on NLP for Conversational AI
Bing Liu | Alexandros Papangelis | Stefan Ultes | Abhinav Rastogi | Yun-Nung Chen | Georgios Spithourakis | Elnaz Nouri | Weiyan Shi
Proceedings of the 4th Workshop on NLP for Conversational AI

EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Georgios Spithourakis | Ivan Vulić | Michał Lis | Inigo Casanueva | Paweł Budzianowski
Findings of the Association for Computational Linguistics: NAACL 2022

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.

NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue
Inigo Casanueva | Ivan Vulić | Georgios Spithourakis | Paweł Budzianowski
Findings of the Association for Computational Linguistics: NAACL 2022

We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements. NLU++ is divided into two domains (BANKING and HOTELS) and brings several crucial improvements over current commonly used NLU datasets. 1) NLU++ provides fine-grained domain ontologies with a large set of challenging multi-intent sentences combined with finer-grained and thus more challenging slot sets. 2) The ontology is divided into domain-specific and generic (i.e., domain-universal) intents that overlap across domains, promoting cross-domain reusability of annotated examples. 3) The dataset design has been inspired by the problems observed in industrial ToD systems, and 4) it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data. Finally, we benchmark a series of current state-of-the-art NLU models on NLU++; the results demonstrate the challenging nature of the dataset, especially in low-data regimes, and call for further research on ToD NLU.

Multi-Label Intent Detection via Contrastive Task Specialization of Sentence Encoders
Ivan Vulić | Iñigo Casanueva | Georgios Spithourakis | Avishek Mondal | Tsung-Hsien Wen | Paweł Budzianowski
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Deploying task-oriented dialog ToD systems for new domains and tasks requires natural language understanding models that are 1) resource-efficient and work under low-data regimes; 2) adaptable, efficient, and quick-to-train; 3) expressive and can handle complex ToD scenarios with multiple user intents in a single utterance. Motivated by these requirements, we introduce a novel framework for multi-label intent detection (mID): MultI-ConvFiT (Multi-Label Intent Detection via Contrastive Conversational Fine-Tuning). While previous work on efficient single-label intent detection learns a classifier on top of a fixed sentence encoder (SE), we propose to 1) transform general-purpose SEs into task-specialized SEs via contrastive fine-tuning on annotated multi-label data, 2) where task specialization knowledge can be stored into lightweight adapter modules without updating the original parameters of the input SE, and then 3) we build improved mID classifiers stacked on top of fixed specialized SEs. Our main results indicate that MultI-ConvFiT yields effective mID models, with large gains over non-specialized SEs reported across a spectrum of different mID datasets, both in low-data and high-data regimes.


PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
Matthew Henderson | Ivan Vulić | Iñigo Casanueva | Paweł Budzianowski | Daniela Gerz | Sam Coope | Georgios Spithourakis | Tsung-Hsien Wen | Nikola Mrkšić | Pei-Hao Su
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts. It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation. We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages.

Training Neural Response Selection for Task-Oriented Dialogue Systems
Matthew Henderson | Ivan Vulić | Daniela Gerz | Iñigo Casanueva | Paweł Budzianowski | Sam Coope | Georgios Spithourakis | Tsung-Hsien Wen | Nikola Mrkšić | Pei-Hao Su
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.

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A Repository of Conversational Datasets
Matthew Henderson | Paweł Budzianowski | Iñigo Casanueva | Sam Coope | Daniela Gerz | Girish Kumar | Nikola Mrkšić | Georgios Spithourakis | Pei-Hao Su | Ivan Vulić | Tsung-Hsien Wen
Proceedings of the First Workshop on NLP for Conversational AI

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using 1-of-100 accuracy. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.


Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers
Georgios Spithourakis | Sebastian Riedel
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary. Our evaluation on clinical and scientific datasets shows that using hierarchical models to distinguish numerals from words improves a perplexity metric on the subset of numerals by 2 and 4 orders of magnitude, respectively, over non-hierarchical models. A combination of strategies can further improve perplexity. Our continuous probability density function model reduces mean absolute percentage errors by 18% and 54% in comparison to the second best strategy for each dataset, respectively.


Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Nasrin Mostafazadeh | Chris Brockett | Bill Dolan | Michel Galley | Jianfeng Gao | Georgios Spithourakis | Lucy Vanderwende
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.


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Clinical Text Prediction with Numerically Grounded Conditional Language Models
Georgios Spithourakis | Steffen Petersen | Sebastian Riedel
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

A Persona-Based Neural Conversation Model
Jiwei Li | Michel Galley | Chris Brockett | Georgios Spithourakis | Jianfeng Gao | Bill Dolan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Numerically Grounded Language Models for Semantic Error Correction
Georgios Spithourakis | Isabelle Augenstein | Sebastian Riedel
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing