Focused Contrastive Training for Test-based Constituency Analysis
We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation that is motivated by constituency theory. We show that consistent gains can be achieved if only certain positive instances are chosen for training, depending on whether they could be the result of a test transformation. This way, the positives, and negatives exhibit similar characteristics, which makes the objective more challenging for the language model, and also allows for additional markup that indicates the position of the test application within the sentence.
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- Roth, Benjamin
- Cano, Erion
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Category |
Paper in Conference Proceedings or in Workshop Proceedings (Poster) |
Event Title |
Thirty-fifth Conference on Neural Information Processing Systems (2021), Workshop on Self-Supervised Learning |
Divisions |
Data Mining and Machine Learning |
Event Location |
virtual-only |
Event Type |
Workshop |
Event Dates |
07 Dec - 10 Dec 2021 |
Date |
7 December 2021 |
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