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

arXiv: https://arxiv.org/abs/2202.00291 · HTML: https://mlsanity.com/paper/2202.00291/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment](https://mlsanity.com/paper/1509.00838/) (2015)
- [WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections](https://mlsanity.com/paper/2012.14919/) (2020)
- [Multi-domain Neural Network Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1603.01232/) (2016)
- [Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization](https://mlsanity.com/paper/1804.07789/) (2018)
- [Partially-Aligned Data-to-Text Generation with Distant Supervision](https://mlsanity.com/paper/2010.01268/) (2020)
- [Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data](https://mlsanity.com/paper/1909.10158/) (2019)
- [Multilingual Fact Linking](https://mlsanity.com/paper/2109.14364/) (2021)

## Built on by (most cited first, 4 of 4)

- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages](https://mlsanity.com/paper/2209.11252/) (2022)
- [Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages](https://mlsanity.com/paper/2302.04790/) (2023)
- [XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages](https://mlsanity.com/paper/2303.12308/) (2023)

## 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.