Doron Cohen


2022

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Conversational Search with Mixed-Initiative - Asking Good Clarification Questions backed-up by Passage Retrieval
Yosi Mass | Doron Cohen | Asaf Yehudai | David Konopnicki
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval from background content to fine-tune two deep-learning models for ranking candidate clarification questions. We evaluated our method on two different use-cases. The first is an open domain conversational search in a large web collection. The second is a task-oriented customer-support setup.We show that our method performs well on both use-cases.

2020

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Conversational Document Prediction to Assist Customer Care Agents
Jatin Ganhotra | Haggai Roitman | Doron Cohen | Nathaniel Mills | Chulaka Gunasekara | Yosi Mass | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users’ needs. We study the task of predicting the documents that customer care agents can use to facilitate users’ needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.

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Agent Assist through Conversation Analysis
Kshitij Fadnis | Nathaniel Mills | Jatin Ganhotra | Haggai Roitman | Gaurav Pandey | Doron Cohen | Yosi Mass | Shai Erera | Chulaka Gunasekara | Danish Contractor | Siva Patel | Q. Vera Liao | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.

2019

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A Summarization System for Scientific Documents
Shai Erera | Michal Shmueli-Scheuer | Guy Feigenblat | Ora Peled Nakash | Odellia Boni | Haggai Roitman | Doron Cohen | Bar Weiner | Yosi Mass | Or Rivlin | Guy Lev | Achiya Jerbi | Jonathan Herzig | Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Francesca Bonin | David Konopnicki
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.