Context-Aware Cross-Lingual Mapping
By Hanan Aldarmaki, Hanan Aldarmaki, Mona T. Diab. 2019
59 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning
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Citations in the corpus
- Cross-lingual Retrieval for Iterative Self-Supervised Training (2020, background): “Cross-Lingual Representations Various previous works have explored leveraging cross-lingual word representations to build parallel dictionaries and phrase tables, then applying them to downstream tasks [1, 2, 3, 4, 31, 36].”