Patrick Healey

Also published as: Pat Healey, Patrick G. T. Healey, Patrick G.T. Healey


2023

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LEDA: a Large-Organization Email-Based Decision-Dialogue-Act Analysis Dataset
Mladen Karan | Prashant Khare | Ravi Shekhar | Stephen McQuistin | Ignacio Castro | Gareth Tyson | Colin Perkins | Patrick Healey | Matthew Purver
Findings of the Association for Computational Linguistics: ACL 2023

Collaboration increasingly happens online. This is especially true for large groups working on global tasks, with collaborators all around the globe. The size and distributed nature of such groups makes decision-making challenging. This paper proposes a set of dialog acts for the study of decision-making mechanisms in such groups, and provides a new annotated dataset based on real-world data from the public mail-archives of one such organisation – the Internet Engineering Task Force (IETF). We provide an initial data analysis showing that this dataset can be used to better understand decision-making in such organisations. Finally, we experiment with a preliminary transformer-based dialog act tagging model.

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Tracing Linguistic Markers of Influence in a Large Online Organisation
Prashant Khare | Ravi Shekhar | Mladen Karan | Stephen McQuistin | Colin Perkins | Ignacio Castro | Gareth Tyson | Patrick Healey | Matthew Purver
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. Here, we investigate a similar question in a large, distributed, consensus-driven community with little traditional power hierarchy – the Internet Engineering Task Force (IETF), a collaborative organisation that designs internet standards. Our analysis based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identify key linguistic differences (e.g., certain LIWC categories, such as “WE” are positively correlated with high-influence). We also identify the differences in language use for the same person before and after becoming influential.

2022

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Embodied Interaction in Mental Health Consultations: Some Observations on Grounding and Repair
Jing Hui Law | Patrick Healey | Rosella Galindo Esparza
Proceedings of the 2022 CLASP Conference on (Dis)embodiment

Shared physical space is an important resource for face-to-face interaction. People use the position and orientation of their bodies—relative to each other and relative to the physical environment—to determine who is part of a conversation, to manage conversational roles (e.g. speaker, addressee, side-participant) and to help co-ordinate turn-taking. These embodied uses of shared space also extend to more fine-grained aspects of interaction, such as gestures and body movements, to support topic management, orchestration of turns and grounding. This paper explores the role of embodied resources in (mis)communication in a corpus of mental health consultations. We illustrate some of the specific ways in which clinicians and patients can exploit embodiment and the position of objects in shared space to diagnose and manage moments of misunderstanding.

2021

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Mitigating Topic Bias when Detecting Decisions in Dialogue
Mladen Karan | Prashant Khare | Patrick Healey | Matthew Purver
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

This work revisits the task of detecting decision-related utterances in multi-party dialogue. We explore performance of a traditional approach and a deep learning-based approach based on transformer language models, with the latter providing modest improvements. We then analyze topic bias in the models using topic information obtained by manual annotation. Our finding is that when detecting some types of decisions in our data, models rely more on topic specific words that decisions are about rather than on words that more generally indicate decision making. We further explore this by removing topic information from the train data. We show that this resolves the bias issues to an extent and, surprisingly, sometimes even boosts performance.

2019

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Proceedings of the IWCS Workshop Vector Semantics for Discourse and Dialogue
Mehrnoosh Sadrzadeh | Matthew Purver | Arash Eshghi | Julian Hough | Ruth Kempson | Patrick G. T. Healey
Proceedings of the IWCS Workshop Vector Semantics for Discourse and Dialogue

2012

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Predicting Adherence to Treatment for Schizophrenia from Dialogue Transcripts
Christine Howes | Matthew Purver | Rose McCabe | Patrick G. T. Healey | Mary Lavelle
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2009

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Proceedings of the SIGDIAL 2009 Conference
Patrick Healey | Roberto Pieraccini | Donna Byron | Steve Young | Matthew Purver
Proceedings of the SIGDIAL 2009 Conference

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A: An Experimental Investigation into... B: ...Split Utterances
Christine Howes | Patrick Healey | Gregory Mills
Proceedings of the SIGDIAL 2009 Conference

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Split Utterances in Dialogue: a Corpus Study
Matthew Purver | Christine Howes | Eleni Gregoromichelaki | Patrick Healey
Proceedings of the SIGDIAL 2009 Conference

2008

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Semantic negotiation in dialogue: the mechanisms of alignment
Gregory Mills | Pat Healey
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

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Quantifying Ellipsis in Dialogue: an index of mutual understanding
Marcus Colman | Arash Eshghi | Pat Healey
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

2007

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Collective States of Understanding
Arash Eshghi | Patrick G.T. Healey
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2003

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Answering Clarification Questions
Matthew Purver | Patrick G.T. Healey | James King | Jonathan Ginzburg | Greg J. Mills
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

2001

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On the Means for Clarification in Dialogue
Matthew Purver | Jonathan Ginzburg | Patrick Healey
Proceedings of the Second SIGdial Workshop on Discourse and Dialogue