Prashant Khare


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.

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.

2014

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Towards Social Event Detection and Contextualisation for Journalists
Prashant Khare | Bahareh Heravi
Proceedings of the First AHA!-Workshop on Information Discovery in Text