Optimizing Word Segmentation for Downstream Task

Tatsuya Hiraoka, Sho Takase, Kei Uchiumi, Atsushi Keyaki, Naoaki Okazaki


Abstract
In traditional NLP, we tokenize a given sentence as a preprocessing, and thus the tokenization is unrelated to a target downstream task. To address this issue, we propose a novel method to explore a tokenization which is appropriate for the downstream task. Our proposed method, optimizing tokenization (OpTok), is trained to assign a high probability to such appropriate tokenization based on the downstream task loss. OpTok can be used for any downstream task which uses a vector representation of a sentence such as text classification. Experimental results demonstrate that OpTok improves the performance of sentiment analysis and textual entailment. In addition, we introduce OpTok into BERT, the state-of-the-art contextualized embeddings and report a positive effect.
Anthology ID:
2020.findings-emnlp.120
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1341–1351
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.120
DOI:
10.18653/v1/2020.findings-emnlp.120
Bibkey:
Cite (ACL):
Tatsuya Hiraoka, Sho Takase, Kei Uchiumi, Atsushi Keyaki, and Naoaki Okazaki. 2020. Optimizing Word Segmentation for Downstream Task. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1341–1351, Online. Association for Computational Linguistics.
Cite (Informal):
Optimizing Word Segmentation for Downstream Task (Hiraoka et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.120.pdf
Code
 tatHi/optok
Data
SNLI