Aman Ahuja


2021

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The First Workshop on Evaluations and Assessments of Neural Conversation Systems
Wei Wei | Bo Dai | Tuo Zhao | Lihong Li | Diyi Yang | Yun-Nung Chen | Y-Lan Boureau | Asli Celikyilmaz | Alborz Geramifard | Aman Ahuja | Haoming Jiang
The First Workshop on Evaluations and Assessments of Neural Conversation Systems

2020

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Question Answering with Long Multiple-Span Answers
Ming Zhu | Aman Ahuja | Da-Cheng Juan | Wei Wei | Chandan K. Reddy
Findings of the Association for Computational Linguistics: EMNLP 2020

Answering questions in many real-world applications often requires complex and precise information excerpted from texts spanned across a long document. However, currently no such annotated dataset is publicly available, which hinders the development of neural question-answering (QA) systems. To this end, we present MASH-QA, a Multiple Answer Spans Healthcare Question Answering dataset from the consumer health domain, where answers may need to be excerpted from multiple, non-consecutive parts of text spanned across a long document. We also propose MultiCo, a neural architecture that is able to capture the relevance among multiple answer spans, by using a query-based contextualized sentence selection approach, for forming the answer to the given question. We also demonstrate that conventional QA models are not suitable for this type of task and perform poorly in this setting. Extensive experiments are conducted, and the experimental results confirm the proposed model significantly outperforms the state-of-the-art QA models in this multi-span QA setting.