mlsanityData to 7 Oct 2026

Bridging Background Knowledge Gaps in Translation with Automatic Explicitation

arXiv 2312.01308 ↗First posted 3 Dec 2023cs.CLcited by 7

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Abstract (arXiv)

Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 3 Dec 2023.

Builds on

17 of 56 references.

References · 17

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 221
  3. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  4. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  5. 2020SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddingscited by 155
  6. 2018Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Informationcited by 110
  7. 2023Benchmarking Machine Translation with Cultural Awarenesscited by 53
  8. 2019Answer-based Adversarial Training for Generating Clarification Questionscited by 42
  9. 2019Quizbowl: The Case for Incremental Question Answeringcited by 36
  10. 2020Elaborative Simplification: Content Addition and Explanation Generation in Text Simplificationcited by 27
  11. 2020Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rankcited by 18
  12. 2023Elaborative Simplification as Implicit Questions Under Discussioncited by 14
5 more
  1. 2013Multilinguals and Wikipedia Editingcited by 9
  2. 2018Identifying Semantic Divergences in Parallel Text without Annotationscited by 9
  3. 2019The Tower of Babel Meets Web 2.0: User-Generated Content and its Applications in a Multilingual Contextcited by 8
  4. 2023Audience-specific Explanations for Machine Translationcited by 3
  5. 2019Analysing Coreference in Transformer Outputscited by 2

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Cited by year

Citing papers per year: 2024: 2; 2025: 5.

7 papers.

  1. 2024Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontierscited by 49
  2. 2024Cultural Adaptation of Menus: A Fine-Grained Approachcited by 8
  3. 2025An Interdisciplinary Approach to Human-Centered Machine Translationcited by 6
  4. 2025Explanatory Summarization with Discourse-Driven Planningcited by 4
  5. 2025Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translationcited by 1
  6. 2025Toward Machine Interpreting: Lessons from Human Interpreting Studiescited by 0
  7. 2025PETra: A Multilingual Corpus of Pragmatic Explicitation in Translationcited by 0