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. |
| Outcome: | The proposed method performs well on low-resource languages with 5M tokens against Hindi . it is compared with existing methods on (mid-resourced) Marathi and Nepali . |
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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. |
| Outcome: | Experiments on 12 language pairs show that the proposed framework improves language alignment by utilizing multiple subspace alignments instead of a single mapping. |
How Lexical is Bilingual Lexicon Induction? (2024.findings-naacl)
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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. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
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. |
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. |
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. |
| Outcome: | The proposed method outperforms existing models on 15 different language pairs on both directions. |
A Structure-Aware Generative Adversarial Network for Bilingual Lexicon Induction (2023.findings-emnlp)
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| Challenge: | Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. |
| Approach: | They propose a model that explicitly captures multiple topological structure information to achieve accurate bilingual lexicon induction. |
| Outcome: | The proposed model captures multiple topological structure information to achieve accurate BLI on a public dataset. |
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment (2021.acl-long)
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| Challenge: | Existing methods for bilingual lexicon induction are linear and require simplifying assumptions. |
| Approach: | They propose methods that combine unsupervised bitext mining and unsupervised word alignment to produce higher quality lexicons. |
| Outcome: | The proposed method outperforms the state-of-the-art on the BUCC 2020 task by 14 F1 points . further analysis suggests they are comparable quality . |
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction (2022.coling-1)
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| Challenge: | Unsupervised bilingual lexicon induction models fail on low-resource language pairs due to insufficient initialization. |
| Approach: | They propose a method to learn cross-lingual features from monolingual corpora for low-resource UBLI by integrating cross-linguistic representations with pre-trained word embeddings in a fully unsupervised initialization. |
| Outcome: | The proposed method outperforms state-of-the-art methods on low-resource language pairs and improves representational ability and robustness of existing embedding models. |
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. |
| Outcome: | The proposed method improves accuracy 6 points over nearest neighbor and 4 points over CSLS retrieval on the same cross-lingual embeddings. |