Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints

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ID: 283110
2023
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Abstract
Lexically-constrained NMT (LNMT) aims to incorporate user-provided terminology into translations. Despite its practical advantages, existing work has not evaluated LNMT models under challenging real-world conditions. In this paper, we focus on two important but under-studied issues that lie in the current evaluation process of LNMT studies. The model needs to cope with challenging lexical constraints that are "homographs" or "unseen" during training. To this end, we first design a homograph disambiguation module to differentiate the meanings of homographs. Moreover, we propose PLUMCOT, which integrates contextually rich information about unseen lexical constraints from pre-trained language models and strengthens a copy mechanism of the pointer network via direct supervision of a copying score. We also release HOLLY, an evaluation benchmark for assessing the ability of a model to cope with "homographic" and "unseen" lexical constraints. Experiments on HOLLY and the previous test setup show the effectiveness of our method. The effects of PLUMCOT are shown to be remarkable in "unseen" constraints. Our dataset is available at https://github.com/papago-lab/HOLLY-benchmark
Reference Key
choo2023towards Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yujin Baek; Koanho Lee; Dayeon Ki; Hyoung-Gyu Lee; Cheonbok Park; Jaegul Choo
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2306.12089v1
Keywords
cs.cl

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