Papers with cross-lingual sentence embeddings
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance (2020.aacl-main)
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| Challenge: | Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They propose an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. |
| Outcome: | The proposed scoring function outperforms baseline methods on high-resource language pairs, 15% on mid-resourced language pairs and 22% on low-resourcing language pairs. |
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment (2024.findings-naacl)
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| Challenge: | Current approaches to obtain cross-lingual sentence embeddings rely on pre-trained language models that implicitly align the contextual representations of similar units of sentences in different languages. |
| Approach: | They propose a framework that explicitly aligns words between English and eight low-resource languages by using off-the-shelf word alignment models. |
| Outcome: | The proposed framework improves on the bitext retrieval task and in high-resource languages. |
Translation Quality Estimation by Jointly Learning to Score and Rank (2020.emnlp-main)
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| Challenge: | The translation quality estimation (QE) task aims to evaluate the general quality of a translation without using reference translations. |
| Approach: | They propose a translation quality estimation task that uses translations as reference . they propose supervised learning using cross-lingual sentence embeddings from pre-trained multilingual models. |
| Outcome: | The proposed model outperforms sentBLEU on the WMT 2019 QE as a Metric task and outperformed sentBLUE on the QE in a multilingual language task. |
Context-Aware Cross-Lingual Mapping (N19-1)
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| Challenge: | 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. |
| Approach: | They propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. |
| Outcome: | The proposed approach outperforms context-independent word mapping in translation retrieval. |
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation (2022.emnlp-main)
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| Challenge: | CAT tools based on translation memories (TMs) are limited in their use for a number of translation tasks due to the limited availability of in-domain TMs. |
| Approach: | They propose a neural approach to exploit in-domain TMs and in-target-language (TL) monolingual corpora to exploit CAT tools. |
| Outcome: | The proposed approach exploits in-domain TMs and in-target-language (TL) monolingual corpora and increases translation proposals on four language pairs. |