The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- The Sensitivity of Language Models and Humans
Final published version, 633 KB, PDF document
Large-scale pretrained language models are the major driving force behind recent improvements in perfromance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones.
Original language | English |
---|---|
Title of host publication | Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics |
Publisher | Association for Computational Linguistics |
Publication date | 2020 |
Pages | 7590-7604 |
DOIs | |
Publication status | Published - 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics - Online Duration: 5 Jul 2020 → 10 Jul 2020 |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics |
---|---|
By | Online |
Periode | 05/07/2020 → 10/07/2020 |
Number of downloads are based on statistics from Google Scholar and www.ku.dk
No data available
ID: 258374819