FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Abstract
Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Cite
@article{arxiv.1810.06683,
title = {FlowQA: Grasping Flow in History for Conversational Machine Comprehension},
author = {Hsin-Yuan Huang and Eunsol Choi and Wen-tau Yih},
journal= {arXiv preprint arXiv:1810.06683},
year = {2019}
}
Comments
15 pages, Published at ICLR 2019