Investigating Length Issues in Document-level Machine Translation

• URL: http://arxiv.org/abs/2412.17592v1
• Date: Mon, 23 Dec 2024 14:08:45 GMT
• Title: Investigating Length Issues in Document-level Machine Translation
• Authors: Ziqian Peng, Rachel Bawden, François Yvon,
• Abstract summary: We design and implement a new approach designed to precisely measure the effect of length increments on machine translation outputs. Experiments show that (a)translation performance decreases with the length of the input text; (b)the position of sentences within the document matters and translation quality is higher for sentences occurring earlier in a document. Our results suggest that even though document-level MT is computationally feasible, it does not yet match the performance of sentence-based MT.
• Score: 19.397788794005862
• License:
• Abstract: Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of MT systems to handle texts comprising up to several thousands of tokens. We design and implement a new approach designed to precisely measure the effect of length increments on MT outputs. Our experiments with two representative architectures unambiguously show that (a)~translation performance decreases with the length of the input text; (b)~the position of sentences within the document matters and translation quality is higher for sentences occurring earlier in a document. We further show that manipulating the distribution of document lengths and of positional embeddings only marginally mitigates such problems. Our results suggest that even though document-level MT is computationally feasible, it does not yet match the performance of sentence-based MT.

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