Sequence Classification with Human Attention
Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, Anders Søgaard
Abstract
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP. Specifically, we use estimated human attention derived from eye-tracking corpora to regularize attention functions in recurrent neural networks. We show substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language.- Anthology ID:
- K18-1030
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Anna Korhonen, Ivan Titov
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 302–312
- Language:
- URL:
- https://aclanthology.org/K18-1030
- DOI:
- 10.18653/v1/K18-1030
- Cite (ACL):
- Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, and Anders Søgaard. 2018. Sequence Classification with Human Attention. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 302–312, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Sequence Classification with Human Attention (Barrett et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/K18-1030.pdf
- Code
- coastalcph/Sequence_classification_with_human_attention
- Data
- FCE