Abari Bhattacharya


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Reference Resolution and New Entities in Exploratory Data Visualization: From Controlled to Unconstrained Interactions with a Conversational Assistant
Abari Bhattacharya | Abhinav Kumar | Barbara Di Eugenio | Roderick Tabalba | Jillian Aurisano | Veronica Grosso | Andrew Johnson | Jason Leigh | Moira Zellner
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

In the context of data visualization, as in other grounded settings, referents are created by the task the agents engage in and are salient because they belong to the shared physical setting. Our focus is on resolving references to visualizations on large displays; crucially, reference resolution is directly involved in the process of creating new entities, namely new visualizations. First, we developed a reference resolution model for a conversational assistant. We trained the assistant on controlled dialogues for data visualizations involving a single user. Second, we ported the conversational assistant including its reference resolution model to a different domain, supporting two users collaborating on a data exploration task. We explore how the new setting affects reference detection and resolution; we compare the performance in the controlled vs unconstrained setting, and discuss the general lessons that we draw from this adaptation.


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LCHQA-Summ: Multi-perspective Summarization of Publicly Sourced Consumer Health Answers
Abari Bhattacharya | Rochana Chaturvedi | Shweta Yadav
Proceedings of the First Workshop on Natural Language Generation in Healthcare

Community question answering forums provide a convenient platform for people to source answers to their questions including those related to healthcare from the general public. The answers to user queries are generally long and contain multiple different perspectives, redundancy or irrelevant answers. This presents a novel challenge for domain-specific concise and correct multi-answer summarization which we propose in this paper.