MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models

• URL: http://arxiv.org/abs/2005.02507v1
• Date: Tue, 5 May 2020 21:30:16 GMT
• Title: MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
• Authors: Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant
• Abstract summary: Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus. This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets.
• Score: 25.398047573530985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al.,2019).This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets. We provide the first systematic retrieval based evaluation over these datasets using two supervised neural models, based on fine-tuning BERT andUSE-QA models respectively, as well as a surprisingly strong information retrieval baseline,BM25. Five of these tasks contain both train-ing and test data, while three contain test data only. Performance on the five tasks with train-ing data shows that while a general model covering all domains is achievable, the best performance is often obtained by training exclusively on in-domain data.

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