Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments
Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Abstract
In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16%, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71%. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.- Anthology ID:
- P19-1635
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6307–6313
- Language:
- URL:
- https://aclanthology.org/P19-1635
- DOI:
- 10.18653/v1/P19-1635
- Cite (ACL):
- Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, and Hsin-Hsi Chen. 2019. Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6307–6313, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (Chen et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-1635.pdf
- Code
- aistairc/Numeracy-600K