Abstract
Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.- Anthology ID:
- Q19-1027
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 7
- Month:
- Year:
- 2019
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Brian Roark, Ani Nenkova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 403–419
- Language:
- URL:
- https://aclanthology.org/Q19-1027
- DOI:
- 10.1162/tacl_a_00277
- Cite (ACL):
- Vered Shwartz and Ido Dagan. 2019. Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition. Transactions of the Association for Computational Linguistics, 7:403–419.
- Cite (Informal):
- Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition (Shwartz & Dagan, TACL 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/Q19-1027.pdf
- Code
- vered1986/lexcomp
- Data
- STREUSLE