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Computer Science > Computation and Language

arXiv:2109.15159 (cs)
[Submitted on 30 Sep 2021]

Title:Focused Contrastive Training for Test-based Constituency Analysis

Authors:Benjamin Roth, Erion Çano
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Abstract: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.
Comments: 4 tables, 19 references
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2109.15159 [cs.CL]
  (or arXiv:2109.15159v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2109.15159

Submission history

From: Erion Çano [view email]
[v1] Thu, 30 Sep 2021 14:22:15 UTC (35 KB)
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