In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explored token-level augmentation. Performed naively, data augmentation can produce semantically incongruent and ungrammatical examples. In this work, we compare simple masked language model replacement and an augmentation method using constituency tree mutations to improve the performance of named entity recognition in low-resource settings with the aim of preserving linguistic cohesion of the augmented sentences.
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Jointly Parse and Fragment Ungrammatical Sentences
This paper is about detecting incorrect arcs in a dependency parse for sentences that contain grammar mistakes. Pruning these arcs results in well-formed parse fragments that can still be useful for downstream applications. We propose two automatic methods that jointly parse the ungrammatical sentence and prune the incorrect arcs: a parser retrained on a parallel corpus of ungrammatical sentences with their corrections, and a sequence-to sequence method. Experimental results show that the proposed strategies are promising for detecting incorrect syntactic dependencies as well as incorrect semantic dependencies.
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- Award ID(s):
- 1735752
- PAR ID:
- 10064139
- Date Published:
- Journal Name:
- Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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