# Multilingual Fact Linking

arXiv: https://arxiv.org/abs/2109.14364 · HTML: https://mlsanity.com/paper/2109.14364/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [Combining pre-trained language models and structured knowledge](https://mlsanity.com/paper/2101.12294/) (2021)
- [The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification](https://mlsanity.com/paper/2010.09381/) (2020)
- [WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web](https://mlsanity.com/paper/2102.09681/) (2021)
- [CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion](https://mlsanity.com/paper/2104.08741/) (2021)

## Built on by (most cited first, 6 of 6)

- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [ComFact: A Benchmark for Linking Contextual Commonsense Knowledge](https://mlsanity.com/paper/2210.12678/) (2022)
- [ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters](https://mlsanity.com/paper/2310.16393/) (2023)
- [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://mlsanity.com/paper/2202.00291/) (2022)
- [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)

## Abstract (arXiv)

Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representation of the fact) in the KG are often present only in a few languages. This makes it challenging to link KG facts to sentences in languages other than the limited set of languages. To address this problem, we introduce the task of Multilingual Fact Linking (MFL) where the goal is to link fact expressed in a sentence to corresponding fact in the KG, even when the fact label in the KG is not available in the language of the sentence. To facilitate research in this area, we present a new evaluation dataset, IndicLink. This dataset contains 11,293 linked WikiData facts and 6,429 sentences spanning English and six Indian languages. We propose a Retrieval+Generation model, ReFCoG, that can scale to millions of KG facts by combining Dual Encoder based retrieval with a Seq2Seq based generation model which is constrained to output only valid KG facts. ReFCoG outperforms standard Retrieval+Re-ranking models by 10.7 pts in Precision@1. In spite of this gain, the model achieves an overall score of 52.1, showing ample scope for improvement in the task.ReFCoG code and IndicLink data are available at https://github.com/SaiKeshav/mfl