Context-Aware Cross-Lingual Mapping
This work addresses the need for better cross-lingual sentence representations for natural language processing applications, though it is incremental as it builds on existing mapping techniques.
The paper tackled the problem of cross-lingual mapping by proposing context-aware methods that map sentence-level embeddings instead of word-level ones, resulting in improved performance in sentence translation retrieval tasks.
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.