Abstract
This paper studies the problem of supporting question answering in a new language with limited training resources. As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively. However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.
Original language | English |
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Title of host publication | EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference |
Publisher | Association for Computational Linguistics |
Pages | 2840-2850 |
Number of pages | 11 |
ISBN (Electronic) | 9781950737901 |
Publication status | Published - 2020 |
Event | 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 - Hong Kong, China Duration: 2019 Nov 3 → 2019 Nov 7 |
Publication series
Name | EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference |
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Conference
Conference | 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 |
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Country | China |
City | Hong Kong |
Period | 19/11/3 → 19/11/7 |
Bibliographical note
Funding Information:We thank NAVER Corporation for sponsoring this project. Partial support received from IITP grant funded by the Korea government (MSIT) (No.2017-0-01779, XAI).
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems