Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

2019/11/06 by Timothy J. Hazen, Shehzaad Dhuliawala, Hazen, Timothy J. +3
Computer Science · #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Seismology and Earthquake Studies #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1911.02655

openalex publication_date 2019/11/06 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28

Abstract

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use in new specialized domains. This paper explores the effectiveness of transfer learning techniques for this problem. In experiments on question answering in the automobile manual domain we demonstrate that standard DNN transfer learning techniques work surprisingly well in adapting DNN models to a new domain using limited amounts of annotated training data in the new domain.

Citations

• Bidirectional Attention Flow for Machine Comprehension
• Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
• Multi-Task Deep Neural Networks for Natural Language Understanding
• XLNet: Generalized Autoregressive Pretraining for Language Understanding

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