Abstract
We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.- Anthology ID:
- 2020.emnlp-main.371
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4587–4603
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.371
- DOI:
- 10.18653/v1/2020.emnlp-main.371
- Cite (ACL):
- Rik van Noord, Antonio Toral, and Johan Bos. 2020. Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4587–4603, Online. Association for Computational Linguistics.
- Cite (Informal):
- Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT (van Noord et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.371.pdf
- Code
- RikVN/Neural_DRS + additional community code