TransPOS: Transformers for Consolidating Different POS Tagset Datasets

• URL: http://arxiv.org/abs/2209.11959v1
• Date: Sat, 24 Sep 2022 08:43:53 GMT
• Title: TransPOS: Transformers for Consolidating Different POS Tagset Datasets
• Authors: Alex Li, Ilyas Bankole-Hameed, Ranadeep Singh, Gabriel Shen Han Ng, Akshat Gupta
• Abstract summary: This paper considers two datasets that label part-of-speech (POS) tags under different tagging schemes. It proposes a novel supervised architecture employing Transformers to tackle the problem of consolidating two completely disjoint datasets.
• Score: 0.8399688944263843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In hope of expanding training data, researchers often want to merge two or more datasets that are created using different labeling schemes. This paper considers two datasets that label part-of-speech (POS) tags under different tagging schemes and leverage the supervised labels of one dataset to help generate labels for the other dataset. This paper further discusses the theoretical difficulties of this approach and proposes a novel supervised architecture employing Transformers to tackle the problem of consolidating two completely disjoint datasets. The results diverge from initial expectations and discourage exploration into the use of disjoint labels to consolidate datasets with different labels.

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