mlsanityData to 7 Oct 2026

XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages

arXiv 2202.00291 ↗First posted 1 Feb 2022cs.CLcited by 4

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Abstract (arXiv)

Multiple critical scenarios (like Wikipedia text generation given English Infoboxes) need automated generation of descriptive text in low resource (LR) languages from English fact triples. Previous work has focused on English fact-to-text (F2T) generation. To the best of our knowledge, there has been no previous attempt on cross-lingual alignment or generation for LR languages. Building an effective cross-lingual F2T (XF2T) system requires alignment between English structured facts and LR sentences. We propose two unsupervised methods for cross-lingual alignment. We contribute XALIGN, an XF2T dataset with 0.45M pairs across 8 languages, of which 5402 pairs have been manually annotated. We also train strong baseline XF2T generation models on the XAlign dataset.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 1 Feb 2022.

Builds on

19 of 45 references.

References · 19

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7 more
  1. 2015What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignmentcited by 31
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  2. 2022XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languagescited by 1
  3. 2023Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languagescited by 1
  4. 2023XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languagescited by 1