Abstract
Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer.- Anthology ID:
- I17-1102
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 1015–1025
- Language:
- URL:
- https://aclanthology.org/I17-1102
- DOI:
- Cite (ACL):
- Nikolaos Pappas and Andrei Popescu-Belis. 2017. Multilingual Hierarchical Attention Networks for Document Classification. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1015–1025, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Multilingual Hierarchical Attention Networks for Document Classification (Pappas & Popescu-Belis, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/I17-1102.pdf
- Code
- idiap/mhan + additional community code