mlsanityData to 7 Oct 2026

Towards Personalised and Document-level Machine Translation of Dialogue

arXiv 2102.10979 ↗First posted 11 Feb 2021cs.CLcited by 4

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Abstract (arXiv)

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker. Because of that, some sentences are translated incorrectly. Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this information into the translation process. Both fields are relatively new and previous work within them is limited. Moreover, there are no readily available robust evaluation metrics for them, which makes it difficult to develop better systems, as well as track global progress and compare different methods. This thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. Three main challenges are addressed: (1) incorporating extra-textual information directly into NMT systems; (2) improving the machine translation of cohesion devices; (3) reliable evaluation for PersNMT and DocNMT.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 63 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

29 of 63 references.

References · 29

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2022FRMT: A Benchmark for Few-Shot Region-Aware Machine Translationcited by 18
  2. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  3. 2021Towards Universality in Multilingual Text Rewritingcited by 7
  4. 2026BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languagescited by 0