Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering

WEN Liang; SHI Haibo; ZHANG Xiaodong; SUN Xin; WEI Xiaochi; WANG Junfeng; CHENG Zhicong; YIN Dawei; WANG Xiaolin; LUO Yingwei; WANG Houfeng

doi:10.1049/cje.2021.00.079

Volume 31 Issue 5

Sep. 2022

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WEN Liang, SHI Haibo, ZHANG Xiaodong, et al., “Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 938-948, 2022, doi: 10.1049/cje.2021.00.079

Citation:

WEN Liang, SHI Haibo, ZHANG Xiaodong, et al., “Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 938-948, 2022, doi: 10.1049/cje.2021.00.079

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Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering

doi: 10.1049/cje.2021.00.079

1.
Department of Computer Science and Technology, Peking University, Beijing 100871, China
2.
Peng Cheng Laboratory, Shenzhen 518052, China
3.
Baidu Inc., Beijing 100193, China

Funds: The work was supported by National Natural Science Foundation of China (62036001, 62032001) and PKU-Baidu Fund (2020BD021).

More Information

Author Bio:
was born in 1990. He is working toward the Ph.D. degree at the School of Electronics Engineering and Computer Science, Peking University. He has great interests in natural language processing and machine learning. Currently, his research areas include question answering and information retrieval. (Email: yuco@pku.edu.cn)

is working as a Senior R&D Engineer in the ranking group at Baidu Inc. He graduated from EECS, Peking University, supervised by Prof. Chao Xu. His main research interests include machine learning, natural language processing, and question answering. (Email: shihaibo@baidu.com)

was born in 1990. He received the Ph.D. degree in computer science from Peking University, Beijing, China. He is currently an R&D Engineer at Baidu Inc. His research interests include question answering and dialogue system and machine learning. (Email: zhangxiaodong11@baidu.com)

was born in 1995. He received the B.E. degree in computer science from Sun Yat-sen University, China. He is currently a Ph.D. candidate at MOE Key Laboratory of Computational Linguistics, Peking University. His main research interests include grammatical error correction and sentence rewriting. (Email: sunx5@pku.edu.cn)

(corresponding author) is a Professor with the School of Electronic Engineering and Computer Science, Peking University (PKU). Now, he is the Director of the Institute of Computational Linguistics of PKU. His research interests include natural language processing and machine learning. (Email: wanghf@pku.edu.cn)
Received Date: 2021-03-01
Accepted Date: 2021-08-31
Rev Recd Date: 2021-08-31

Available Online: 2021-11-09

Publish Date: 2022-09-05

Abstract

Abstract

Phrase-indexed question answering (PIQA) seeks to improve the inference speed of question answering (QA) models by enforcing complete independence of the document encoder from the question encoder, and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework—AnsDR, which consists of an answer boundary detector (AnsD) and an answer candidate ranker (AnsR). More specifically, AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task.
- Real-time question answering,
- Answer boundary detection,
- Answer re-ranking,
- Phrase-indexed question answering,
- Answer span aware loss

Note that the accuracy of both boundaries is different from the Exact Match metric^[2]. Exact Match determines if the predicted answer span is literally the same as the target answer span which does not take the positions of answer span into consideration.
Theoretically, for a document with $ m $ words, the number of all possible answer phrases is $ O\left(m^{2}\right) $. In practice, to efficiently compute and store the answer phrase representations, mainstream approaches represent answer phrases as the concatenation of corresponding start word and end word representations.
For convenience and simplicity, we don’t emphasize the difference between sub-word token and word token.
As in AnsDR, we first run our reimplemented “DENSPI” model to obtain rough answer boundaries and adopt the large candidate expansion strategy to expand candidates. Then, we use our answer re-ranker that was jointly trained with “DENSPI” to select the best.
So far, there are no public evaluation results on the NewsQA dataset which follow the independence restrictions from PIQA.
For some questions, more than one answer is correct.

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References(27)

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