Yanchao Yu


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The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues
Yuru Jiang | Yang Xu | Yuhang Zhan | Weikai He | Yilin Wang | Zixuan Xi | Meiyun Wang | Xinyu Li | Yu Li | Yanchao Yu
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We describe a new freely available Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. The data has been extracted from the original TV scripts of a Chinese sitcom called “I Love My Home” with complex family-based human daily spoken conversations in Chinese. First, we introduced human annotation scheme for both global Character relationship map and character reference relationship. And then we generated the dialogue-based character relationship triples. The corpus annotates relationships between 140 entities in total. We also carried out a data exploration experiment by deploying a BERT-based model to extract character relationships on the CRECIL corpus and another existing relation extraction corpus (DialogRE (CITATION)).The results demonstrate that extracting character relationships is more challenging in CRECIL than in DialogRE.

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A Visually-Aware Conversational Robot Receptionist
Nancie Gunson | Daniel Hernandez Garcia | Weronika Sieińska | Angus Addlesee | Christian Dondrup | Oliver Lemon | Jose L. Part | Yanchao Yu
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Socially Assistive Robots (SARs) have the potential to play an increasingly important role in a variety of contexts including healthcare, but most existing systems have very limited interactive capabilities. We will demonstrate a robot receptionist that not only supports task-based and social dialogue via natural spoken conversation but is also capable of visually grounded dialogue; able to perceive and discuss the shared physical environment (e.g. helping users to locate personal belongings or objects of interest). Task-based dialogues include check-in, navigation and FAQs about facilities, alongside social features such as chit-chat, access to the latest news and a quiz game to play while waiting. We also show how visual context (objects and their spatial relations) can be combined with linguistic representations of dialogue context, to support visual dialogue and question answering. We will demonstrate the system on a humanoid ARI robot, which is being deployed in a hospital reception area.


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A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AI
Angus Addlesee | Yanchao Yu | Arash Eshghi
Proceedings of the 28th International Conference on Computational Linguistics

Automatic Speech Recognition (ASR) systems are increasingly powerful and more accurate, but also more numerous with several options existing currently as a service (e.g. Google, IBM, and Microsoft). Currently the most stringent standards for such systems are set within the context of their use in, and for, Conversational AI technology. These systems are expected to operate incrementally in real-time, be responsive, stable, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this paper we evaluate the most popular of such systems with metrics and experiments designed with these standards in mind. We also evaluate the speaker diarization (SD) capabilities of the same systems which will be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR system which preserves disfluent materials and IBM has the leading incremental SD system in addition to the ASR that is most robust to speech overlaps. Google strikes a balance between the two but none of these systems are yet suitable to reliably handle natural spontaneous conversations in real-time.


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The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
Yanchao Yu | Arash Eshghi | Gregory Mills | Oliver Lemon
Proceedings of the Sixth Workshop on Vision and Language

We motivate and describe a new freely available human-human dialogue data set for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; anon.) with a novel task, where a Learner needs to learn invented visual attribute words (such as “burchak” for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self- and other-correction, mid-sentence continuations, interruptions, turn overlaps, fillers, hedges and many kinds of ellipsis. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental dialogue data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.

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Learning how to Learn: An Adaptive Dialogue Agent for Incrementally Learning Visually Grounded Word Meanings
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the First Workshop on Language Grounding for Robotics

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users, and achieve good learning performance (i.e. accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus – a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.

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VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We present VOILA: an optimised, multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human user. VOILA is: (1) able to learn new visual categories interactively from users from scratch; (2) trained on real human-human dialogues in the same domain, and so is able to conduct natural spontaneous dialogue; (3) optimised to find the most effective trade-off between the accuracy of the visual categories it learns and the cost it incurs to users. VOILA is deployed on Furhat, a human-like, multi-modal robot head with back-projection of the face, and a graphical virtual character.


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Interactively Learning Visually Grounded Word Meanings from a Human Tutor
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the 5th Workshop on Vision and Language

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Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Incremental Generation of Visually Grounded Language in Situated Dialogue (demonstration system)
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the 9th International Natural Language Generation conference


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Comparing Attribute Classifiers for Interactive Language Grounding
Yanchao Yu | Arash Eshghi | Oliver Lemon
Proceedings of the Fourth Workshop on Vision and Language


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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)