Jonathan Gratch


2022

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Opponent Modeling in Negotiation Dialogues by Related Data Adaptation
Kushal Chawla | Gale Lucas | Jonathan May | Jonathan Gratch
Findings of the Association for Computational Linguistics: NAACL 2022

Opponent modeling is the task of inferring another party’s mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent’s preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.

2021

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CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
Kushal Chawla | Jaysa Ramirez | Rene Clever | Gale Lucas | Jonathan May | Jonathan Gratch
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo

2018

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The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents
Ron Artstein | Jill Boberg | Alesia Gainer | Jonathan Gratch | Emmanuel Johnson | Anton Leuski | Gale Lucas | David Traum
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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The Distress Analysis Interview Corpus of human and computer interviews
Jonathan Gratch | Ron Artstein | Gale Lucas | Giota Stratou | Stefan Scherer | Angela Nazarian | Rachel Wood | Jill Boberg | David DeVault | Stacy Marsella | David Traum | Skip Rizzo | Louis-Philippe Morency
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder. The interviews are conducted by humans, human controlled agents and autonomous agents, and the participants include both distressed and non-distressed individuals. Data collected include audio and video recordings and extensive questionnaire responses; parts of the corpus have been transcribed and annotated for a variety of verbal and non-verbal features. The corpus has been used to support the creation of an automated interviewer agent, and for research on the automatic identification of psychological distress.

2010

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Cross-Domain Speech Disfluency Detection
Kallirroi Georgila | Ning Wang | Jonathan Gratch
Proceedings of the SIGDIAL 2010 Conference

2005

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Dealing with Doctors: A Virtual Human for Non-team Interaction
David Traum | William Swartout | Jonathan Gratch | Stacy Marsella | Patrick Kenny | Eduard Hovy | Shri Narayanan | Ed Fast | Bilyana Martinovski | Rahul Baghat | Susan Robinson | Andrew Marshall | Dagen Wang | Sudeep Gandhe | Anton Leuski
Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue