Christian Dondrup


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Multi-party Multimodal Conversations Between Patients, Their Companions, and a Social Robot in a Hospital Memory Clinic
Angus Addlesee | Neeraj Cherakara | Nivan Nelson | Daniel Hernandez Garcia | Nancie Gunson | Weronika Sieińska | Christian Dondrup | Oliver Lemon
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

We have deployed an LLM-based spoken dialogue system in a real hospital. The ARI social robot embodies our system, which patients and their companions can have multi-party conversations with together. In order to enable this multi-party ability, multimodality is critical. Our system, therefore, receives speech and video as input, and generates both speech and gestures (arm, head, and eye movements). In this paper, we describe our complex setting and the architecture of our dialogue system. Each component is detailed, and a video of the full system is available with the appropriate components highlighted in real-time. Our system decides when it should take its turn, generates human-like clarification requests when the patient pauses mid-utterance, answers in-domain questions (grounding to the in-prompt knowledge), and responds appropriately to out-of-domain requests (like generating jokes or quizzes). This latter feature is particularly remarkable as real patients often utter unexpected sentences that could not be handled previously.


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SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation
Bhathiya Hemanthage | Christian Dondrup | Phil Bartie | Oliver Lemon
Proceedings of the 15th International Conference on Computational Semantics

SimpleMTOD is a simple language model which recasts several sub-tasks in multimodal task-oriented dialogues as sequence prediction tasks. SimpleMTOD is built on a large-scale transformer-based auto-regressive architecture, which has already proven to be successful in uni-modal task-oriented dialogues, and effectively leverages transfer learning from pretrained GPT-2. In-order to capture the semantics of visual scenes, we introduce both local and de-localized tokens for objects within a scene. De-localized tokens represent the type of an object rather than the specific object itself and so possess a consistent meaning across the dataset. SimpleMTOD achieves a state-of-the-art BLEU score (0.327) in the Response Generation sub-task of the SIMMC 2.0 test-std dataset while performing on par in other multimodal sub-tasks: Disambiguation, Coreference Resolution, and Dialog State Tracking. This is despite taking a minimalist approach for extracting visual (and non-visual) informa- tion. In addition the model does not rely on task-specific architectural changes such as classification heads.

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Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering
Angus Addlesee | Weronika Sieińska | Nancie Gunson | Daniel Hernandez Garcia | Christian Dondrup | Oliver Lemon
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

This paper evaluates the extent to which current LLMs can capture task-oriented multi-party conversations (MPCs). We have recorded and transcribed 29 MPCs between patients, their companions, and a social robot in a hospital. We then annotated this corpus for multi-party goal-tracking and intent-slot recognition. People share goals, answer each other’s goals, and provide other people’s goals in MPCs - none of which occur in dyadic interactions. To understand user goals in MPCs, we compared three methods in zero-shot and few-shot settings: we fine-tuned T5, created pre-training tasks to train DialogLM using LED, and employed prompt engineering techniques with GPT-3.5-turbo, to determine which approach can complete this novel task with limited data. GPT-3.5-turbo significantly outperformed the others in a few-shot setting. The ‘reasoning’ style prompt, when given 7% of the corpus as example annotated conversations, was the best performing method. It correctly annotated 62.32% of the goal tracking MPCs, and 69.57% of the intent-slot recognition MPCs. A ‘story’ style prompt increased model hallucination, which could be detrimental if deployed in safety-critical settings. We conclude that multi-party conversations still challenge state-of-the-art LLMs.


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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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Conversational Agents for Intelligent Buildings
Weronika Sieińska | Christian Dondrup | Nancie Gunson | Oliver Lemon
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We will demonstrate a deployed conversational AI system that acts as a host of a smart-building on a university campus. The system combines open-domain social conversation with task-based conversation regarding navigation in the building, live resource updates (e.g. available computers) and events in the building. We are able to demonstrate the system on several platforms: Google Home devices, Android phones, and a Furhat robot.


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Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Jekaterina Novikova | Christian Dondrup | Ioannis Papaioannou | Oliver Lemon
Proceedings of the First Workshop on Language Grounding for Robotics

Recognition of social signals, coming from human facial expressions or prosody of human speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users’ impressions of the robot after a conversation. We find that happiness in the user’s recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that the facial expression emotional features and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning-based adaptive human-robot dialogue systems.