Abstract
We present novel advancements in frame-semantic parsing, specifically focusing on target identification and frame identification. Our target identification model employs a novel prefix tree modification to enable robust support for multi-word lexical units, resulting in a coverage of 99.4% of the targets in the FrameNet 1.7 fulltext annotations. It utilizes a RoBERTa-based filter to achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. For frame identification, we introduce a modification to the standard multiple-choice classification paradigm by incorporating additional negative frames for targets with limited candidate frames, resulting in a +0.014 accuracy improvement over the frame-only model of FIDO, the previous state-of-the-art system, and +0.002 over its full system. Our approach significantly enhances performance on rare frames, exhibiting an improvement of +0.044 over FIDO’s accuracy on frames with 5 or fewer samples, and on under-utilized frames, with an improvement of +0.139 on targets with a single candidate frame. Overall, our contributions address critical challenges and advance the state-of-the-art in frame-semantic parsing.- Anthology ID:
- 2024.acl-long.374
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6930–6941
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.374
- DOI:
- 10.18653/v1/2024.acl-long.374
- Cite (ACL):
- Jacob Devasier, Yogesh Gurjar, and Chengkai Li. 2024. Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6930–6941, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (Devasier et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.acl-long.374.pdf