# Context-Aware Cross-Lingual Mapping

arXiv: https://arxiv.org/abs/1903.03243 · HTML: https://mlsanity.com/paper/1903.03243/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 17 of 17)

- [Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training](https://mlsanity.com/paper/2104.08645/) (2021)
- [Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings](https://mlsanity.com/paper/2110.02887/) (2021)
- [Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings](https://mlsanity.com/paper/2311.18034/) (2023)
- [Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction](https://mlsanity.com/paper/2106.03084/) (2021)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [Combining Static and Contextualised Multilingual Embeddings](https://mlsanity.com/paper/2203.09326/) (2022)
- [The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer](https://mlsanity.com/paper/2204.06457/) (2022)
- [Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining](https://mlsanity.com/paper/2104.07642/) (2021)
- [Cross-Lingual Text Classification of Transliterated Hindi and Malayalam](https://mlsanity.com/paper/2108.13620/) (2021)
- [ALGEN: Few-shot Inversion Attacks on Textual Embeddings using Alignment and Generation](https://mlsanity.com/paper/2502.11308/) (2025)
- [Cross-lingual alignments of ELMo contextual embeddings](https://mlsanity.com/paper/2106.15986/) (2021)
- [GRI: Graph-based Relative Isomorphism of Word Embedding Spaces](https://mlsanity.com/paper/2310.12360/) (2023)
- [GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings](https://mlsanity.com/paper/2310.13068/) (2023)
- [Discrete Cosine Transform as Universal Sentence Encoder](https://mlsanity.com/paper/2106.00934/) (2021)
- [Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations](https://mlsanity.com/paper/2201.13429/) (2022)
- [Mismatching-Aware Unsupervised Translation Quality Estimation For Low-Resource Languages](https://mlsanity.com/paper/2208.00463/) (2022)
- [Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models](https://mlsanity.com/paper/2505.23146/) (2025)

## Abstract (arXiv)

Cross-lingual word vectors are typically obtained by fitting an orthogonal matrix that maps the entries of a bilingual dictionary from a source to a target vector space. Word vectors, however, are most commonly used for sentence or document-level representations that are calculated as the weighted average of word embeddings. In this paper, we propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity. We incorporate context in the transformation matrix by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. We also implement cross-lingual mapping of deep contextualized word embeddings using parallel sentences with word alignments. In our experiments, both approaches resulted in cross-lingual sentence embeddings that outperformed context-independent word mapping in sentence translation retrieval. Furthermore, the sentence-level transformation could be used for word-level mapping without loss in word translation quality.