Papers with multilingual embedding
Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces (2025.naacl-short)
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| Challenge: | a new study characterizes the preservation of intertextuality across human and machine translations . intertextual references can range from direct quotation to semantic resemblance, both within and between texts . |
| Approach: | They use multilingual embedding spaces to characterize preservation of intertextuality . they use biblical texts, which are both full of inter textual references . |
| Outcome: | The proposed method characterizes preservation of intertextuality across human and machine translations. |
The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction (2022.findings-emnlp)
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| Challenge: | Prior work on inductive biases of language models towards natural language has focused on quantifying their ability to build multilingual spaces. |
| Approach: | They propose to use linguistically motivated tasks as a proxy to study inductive biases of language models with respect to natural language phenomena to build multilingual embedding spaces. |
| Outcome: | The proposed model performance is compared with other models using a set of linguistically motivated tasks and a training corpus in 15 languages. |
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation (2021.emnlp-main)
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| Challenge: | Existing methods to extract language-specific information from multilingual sentence embeddings are remarkably successful in cross-lingual and multilingual NLU tasks. |
| Approach: | They propose to extract language-specific information from the original embedding and use it to retrieve an embeddable that fully represents the sentence’s meaning. |
| Outcome: | The proposed method outperforms baselines on cross-lingual sentences even in low-resource language pairs where only tens of thousands of parallel sentence pairs are available. |
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |