A Comparison of Approaches to Document-level Machine Translation

• URL: http://arxiv.org/abs/2101.11040v1
• Date: Tue, 26 Jan 2021 19:21:09 GMT
• Title: A Comparison of Approaches to Document-level Machine Translation
• Authors: Zhiyi Ma, Sergey Edunov, Michael Auli
• Abstract summary: This paper presents a systematic comparison of selected approaches to document-level phenomena evaluation suites. We find that a simple method based purely on back-translating monolingual document-level data performs as well as much more elaborate alternatives.
• Score: 34.2276281264886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level machine translation conditions on surrounding sentences to produce coherent translations. There has been much recent work in this area with the introduction of custom model architectures and decoding algorithms. This paper presents a systematic comparison of selected approaches from the literature on two benchmarks for which document-level phenomena evaluation suites exist. We find that a simple method based purely on back-translating monolingual document-level data performs as well as much more elaborate alternatives, both in terms of document-level metrics as well as human evaluation.

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