Stephanie M. Lukin, Felix Gervits, Cory J. Hayes, Pooja Moolchandani, Anton Leuski, John G. Rogers III, Carlos Sanchez Amaro, Matthew Marge, Clare R. Voss, David Traum
| Challenge: | Demo will allow users to issue unconstrained spoken language commands to ScoutBot. |
| Approach: | The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. |
| Outcome: | The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. |
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SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)
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Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor A. Hudson, Cory J. Hayes, Kimberly Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare Voss
| Challenge: | The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
| Approach: | They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. |
| Outcome: | The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
A Research Platform for Multi-Robot Dialogue with Humans (N19-4)
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Matthew Marge, Stephen Nogar, Cory J. Hayes, Stephanie M. Lukin, Jesse Bloecker, Eric Holder, Clare Voss
| Challenge: | a new research platform supports spoken dialogue interaction with multiple robots . a ground robot and an aerial robot are used to perform search and rescue tasks . |
| Approach: | They propose a platform that supports spoken dialogue interaction with multiple robots . they use existing tools for speech recognition and dialogue management . |
| Outcome: | The proposed platform supports spoken dialogue interaction with multiple robots in a search and rescue scenario. |
Cue-bot: A Conversational Agent for Assistive Technology (2022.acl-demo)
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Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, Lama Nachman
| Challenge: | Large-scale pre-training has achieved significant performance gains across many tasks within NLP, including intent prediction and dialogue state tracking. |
| Approach: | They propose to use eye-tracking, mouse controls and an intelligent agent Cue-bot to represent the user in a conversation. |
| Outcome: | The proposed system can be used by people with different levels of disabilities to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
Polite Chatbot: A Text Style Transfer Application (2023.eacl-srw)
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| Challenge: | Creating polite chatbots requires complex setups that require reinforcement learning to produce coherent responses. |
| Approach: | They propose a polite chatbot that can generate coherent responses to given contexts by using a model that transfers neutral sentences into polite ones and trains a dialogue model. |
| Outcome: | The proposed method outperforms baselines in producing polite dialogue responses while staying competitive in terms of coherent to the given context. |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
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Deep Learning for Dialogue Systems (C18-3)
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| Challenge: | Using deep learning to build robust and scalable spoken dialogue systems is still a challenging task. |
| Approach: | tutorial focuses on an overview of dialogue system development . goal-oriented spoken dialogue systems are most prominent component in virtual personal assistants . |
| Outcome: | This tutorial focuses on an overview of dialogue system development while summarizing the challenges. |
Grammar Control in Dialogue Response Generation for Language Learning Chatbots (2025.naacl-long)
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| Challenge: | Existing language learning chatbots and research on second language acquisition benefit from these affordances. |
| Approach: | They ground a dialogue response generation model in a pedagogical repository of grammar skills and evaluate prompting, fine-tuning, and decoding strategies for grammar-controlled dialogue response generators. |
| Outcome: | The proposed model outperforms GPT-3.5 when tolerating minor response quality losses and predicts grammar-controlled responses to support grammar acquisition adapted to learner proficiency. |
Injecting Salesperson’s Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning (2024.findings-acl)
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| Challenge: | Recent research in dialogue systems focuses on task-oriented (TOD) and open-domain (chit-chat) dialogues. |
| Approach: | They propose to use chit-chat to simulate task-oriented dialogues to train sales agents. |
| Outcome: | The proposed model improves coherence and reduces aggression, improving model learning for sales-customer interactions. |
StuBot: Learning by Teaching a Conversational Agent Through Machine Reading Comprehension (2022.findings-emnlp)
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| Challenge: | StuBot provides adaptive feedback for learning by teaching . |
| Approach: | They propose a text-based conversational agent that provides adaptive feedback for learning by teaching. |
| Outcome: | The proposed agent improves learning performance, immersion, and overall experience by providing adaptive feedback to the users who input the explanation text. |