Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (2022.findings-emnlp)
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| Challenge: | Current methods for bilingual lexicon induction rely on the induction of cross-lingual word embeddings (CLWEs) such as VecMap or mPLMs are not available for multilingual NLP. |
| Approach: | They propose a semi-supervised post-hoc reranking method which combines cross-lingual lexical knowledge from multilingual pretrained language models with original CLWEs. |
| Outcome: | The proposed method outperforms existing methods on two standard benchmarks spanning a wide spectrum of languages and is robust to different CLWEs. |
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| Challenge: | lexical variation and low-resource settings make it difficult to learn in low-level settings. |
| Approach: | They propose to incorporate additional lexical information into the retrieve-and-rank approach to improve lexicon induction. |
| Outcome: | The proposed approach improves on XLING by an average of 2% across all language pairs. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
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| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
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LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space (2020.emnlp-main)
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| Challenge: | Existing methods for bilingual lexicon induction are mapping-based, but they do not hold for closely related languages. |
| Approach: | They propose a semi-supervised method to learn cross-lingual word embeddings for BLI using a linear mapping function and a latent space of two independently trained autoencoders. |
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Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction (2020.coling-main)
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| Challenge: | Bilingual dictionary induction (BDI) is a task of finding target language translations of source language words. |
| Approach: | They propose to use bilingual orthography Embeddings to enrich BWE-based BDI with transliteration information to make a decision on which information source is more reliable for a particular word pair. |
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Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces (P19-1)
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| Challenge: | Recent work on bilingual lexicon induction (BLI) relies on an assumption about the isometry of two embedding spaces. |
| Approach: | They propose a semi-supervised approach that relaxes the isometric assumption while leveraging limited aligned bilingual lexicons and a larger set of unaligned word embeddings. |
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On Bilingual Lexicon Induction with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing approaches to induction bilingual lexicons still require cross-lingual word representations . a recent study shows that few-shot prompting with in-context examples from nearest neighbours achieves the best performance . |
| Approach: | They examine whether it is possible to prompt and fine-tune multilingual LLMs for BLI . they experiment with 18 open-source text-to-text mLLMs of different sizes . |
| Outcome: | The proposed approach is compared with existing approaches on two standard BLI benchmarks covering a range of typologically diverse languages. |
Bilingual Lexicon Induction through Unsupervised Machine Translation (P19-1)
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| Challenge: | Existing methods for bilingual lexicon induction use nearest neighbor or related retrieval methods to induce word translation pairs. |
| Approach: | They propose a method that aligns word embeddings in two languages and uses them to build a phrase-table and a language model to extract the bilingual lexicon. |
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When Your Cousin Has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages (2024.lrec-main)
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| Challenge: | Existing methods for unsupervised bilingual lexicon induction depend on good quality static or contextual embeddings for both languages. |
| Approach: | They propose a method for unsupervised bilingual lexicon induction between a related LRL and a high-resource language that only requires inference on a masked language model of the HRL. |
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
| Approach: | They evaluate CLE models for a large number of language pairs on bilingual lexicon induction and three downstream tasks. |
| Outcome: | The proposed model performance is based on supervised and unsupervised models on bilingual lexicon induction and three downstream tasks. |
DM-BLI: Dynamic Multiple Subspaces Alignment for Unsupervised Bilingual Lexicon Induction (2024.acl-long)
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| Challenge: | Existing approaches to unsupervised bilingual lexicon induction (BLI) fail on distant or low-resource language pairs, achieving less than half the performance observed in rich-resourced languages. |
| Approach: | They propose a framework for unsupervised bilingual lexicon induction that uses multiple subspace alignments instead of a single mapping. |
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