Ruchi Mahindru


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Dynamic Facet Selection by Maximizing Graded Relevance
Michael Glass | Md Faisal Mahbub Chowdhury | Yu Deng | Ruchi Mahindru | Nicolas Rodolfo Fauceglia | Alfio Gliozzo | Nandana Mihindukulasooriya
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing

Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determined at runtime based on the context of user query instead of pre-indexing the facets statically. In this paper, we propose a new unsupervised approach for dynamic facet generation, namely optimistic facets, which attempts to generate the best possible subset of facets, hence maximizing expected Discounted Cumulative Gain (DCG), a measure of ranking quality that uses a graded relevance scale. We also release code to generate a new evaluation dataset. Through empirical results on two datasets, we show that the proposed DFS approach considerably improves the document ranking in the search results.

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Technical Question Answering across Tasks and Domains
Wenhao Yu | Lingfei Wu | Yu Deng | Qingkai Zeng | Ruchi Mahindru | Sinem Guven | Meng Jiang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.


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A Technical Question Answering System with Transfer Learning
Wenhao Yu | Lingfei Wu | Yu Deng | Ruchi Mahindru | Qingkai Zeng | Sinem Guven | Meng Jiang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In recent years, the need for community technical question-answering sites has increased significantly. However, it is often expensive for human experts to provide timely and helpful responses on those forums. We develop TransTQA, which is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past. TransTQA is built upon a siamese ALBERT network, which enables it to respond quickly and accurately. Furthermore, TransTQA adopts a standard deep transfer learning strategy to improve its capability of supporting multiple technical domains.