# Context-Aware Cross-Lingual Mapping

## TL;DR

This paper introduces a context-aware approach for cross-lingual mapping that improves sentence translation retrieval by directly aligning sentence embeddings, outperforming traditional word-level mapping methods.

## Contribution

It proposes a novel sentence-level cross-lingual mapping method that incorporates context, enhancing translation retrieval accuracy over existing word-level approaches.

## Key findings

- Sentence-level mapping outperforms word-level mapping in translation retrieval.
- Context-aware embeddings improve cross-lingual similarity measures.
- Deep contextualized embeddings benefit from sentence-level alignment.

## Abstract

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.