Exploring Discourse Structure in Document-level Machine Translation
Submission Type: Regular Long Paper
Submission Track: Machine Translation
Keywords: Document-level Machine Translation, Discourse Structure, RST Parsing
TL;DR: We propose a more sound paragraph-to-paragraph mode and RST-Att model with a multi-granularity attention mechanism based on RST parsing for DocMT.
Abstract: Neural machine translation has achieved great success in the past few years with the help of transformer architectures and large-scale bilingual corpora. However, when the source text gradually grows into an entire document, the performance of current methods for document-level machine translation (DocMT) is less satisfactory. Although the context is beneficial to the translation in general, it is difficult for traditional methods to utilize such long-range information. Previous studies on DocMT have concentrated on extra contents such as multiple surrounding sentences and input instances divided by a fixed length. We suppose that they ignore the structure inside the source text, which leads to under-utilization of the context. In this paper, we present a more sound paragraph-to-paragraph translation mode and explore whether discourse structure can improve DocMT. We introduce several methods from different perspectives, among which our RST-Att model with a multi-granularity attention mechanism based on the RST parsing tree works best. The experiments show that our method indeed utilizes discourse information and performs better than previous work.
Submission Number: 2890
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