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arXiv · 2004.01909

Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models

Sheng-Chieh Lin·Jheng-Hong Yang·Rodrigo Nogueira·Ming-Feng Tsai·Chuan-Ju Wang·Jimmy Lin
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Abstract

This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with different numbers of parameters, we demonstrate that the recent text-to-text transfer transformer (T5) achieves the best results both on CANARD and CAsT with fewer parameters, compared to similar transformer architectures.

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Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin. 2020-04-04. Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models. https://arxiv.org/abs/2004.01909

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