Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

• URL: http://arxiv.org/abs/2112.06096v1
• Date: Sat, 11 Dec 2021 23:29:26 GMT
• Title: Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts
• Authors: Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Pieter Spronck
• Abstract summary: We propose a method for selecting in-domain data from generic-domain (parallel text) corpora for the task of machine translation. The proposed method ranks sentences in parallel general-domain data according to their cosine similarity with a monolingual domain-specific data set. We then select the top K sentences with the highest similarity score to train a new machine translation system tuned to the specific in-domain data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Continuously-growing data volumes lead to larger generic models. Specific use-cases are usually left out, since generic models tend to perform poorly in domain-specific cases. Our work addresses this gap with a method for selecting in-domain data from generic-domain (parallel text) corpora, for the task of machine translation. The proposed method ranks sentences in parallel general-domain data according to their cosine similarity with a monolingual domain-specific data set. We then select the top K sentences with the highest similarity score to train a new machine translation system tuned to the specific in-domain data. Our experimental results show that models trained on this in-domain data outperform models trained on generic or a mixture of generic and domain data. That is, our method selects high-quality domain-specific training instances at low computational cost and data size.

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