# Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations

arXiv: https://arxiv.org/abs/2004.14923 · HTML: https://mlsanity.com/paper/2004.14923/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 21 of 21)

- [Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?](https://mlsanity.com/paper/2409.19151/) (2024)
- [Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens](https://mlsanity.com/paper/2305.11550/) (2023)
- [Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation](https://mlsanity.com/paper/2209.15236/) (2022)
- [LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization](https://mlsanity.com/paper/2401.06034/) (2024)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [Does Typological Blinding Impede Cross-Lingual Sharing?](https://mlsanity.com/paper/2101.11888/) (2021)
- [Language Embeddings Sometimes Contain Typological Generalizations](https://mlsanity.com/paper/2301.08115/) (2023)
- [Breaking Down Multilingual Machine Translation](https://mlsanity.com/paper/2110.08130/) (2021)
- [GradSim: Gradient-Based Language Grouping for Effective Multilingual Training](https://mlsanity.com/paper/2310.15269/) (2023)
- [Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation](https://mlsanity.com/paper/2104.05848/) (2021)
- [Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation](https://mlsanity.com/paper/2203.10250/) (2022)
- [Towards a Deep Understanding of Multilingual End-to-End Speech Translation](https://mlsanity.com/paper/2310.20456/) (2023)
- [Neighbors and relatives: How do speech embeddings reflect linguistic connections across the world?](https://mlsanity.com/paper/2506.08564/) (2025)
- [Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?](https://mlsanity.com/paper/2110.07816/) (2021)
- [Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix](https://mlsanity.com/paper/2312.02820/) (2023)
- [Informative Language Representation Learning for Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/2209.01530/) (2022)
- [LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2406.02876/) (2024)
- [Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?](https://mlsanity.com/paper/2112.12356/) (2021)
- [Typologically-Informed Candidate Reranking for LLM-based Translation into Low-Resource Languages](https://mlsanity.com/paper/2602.01162/) (2026)
- [Conditioned in Training, Dispensable at Inference: Typological Conditioning for Multilingual Speech Translation](https://mlsanity.com/paper/2605.16026/) (2026)
- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other's language characterisation. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, which is also released as a tool, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.