Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to learn orthogonal matrix aligning bilingual lexicons are suboptimal . resulting models suffer from "hubness problem" because word vectors tend to be nearest neighbors of abnormally high number of other words. |
| Approach: | They propose a unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. |
| Outcome: | The proposed approach outperforms the state-of-the-art on word translation on standard benchmarks. |
Similar Papers
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
| Outcome: | The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks. |
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment (2021.acl-long)
Copied to clipboard
| 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 . |
A Locally Linear Procedure for Word Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to learn word embeddings of two languages are limited by the expressiveness of the translation model. |
| Approach: | They propose an algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matric. |
| Outcome: | The proposed algorithm achieves better performance in bilingual and cross-lingual word translation tasks compared to the single matrix baseline. |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings (P19-1)
Copied to clipboard
| Challenge: | Existing methods for cross-lingual word embeddings have limited results . existing methods require little or no cross-linguistic signal to work . |
| Approach: | They compare offline mapping methods to an extension of skip-gram that jointly learns both embedding spaces. |
| Outcome: | The proposed method yields more isomorphic embeddings, is less sensitive to hubness, and achieves stronger results in bilingual lexicon induction. |
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
How Lexical is Bilingual Lexicon Induction? (2024.findings-naacl)
Copied to clipboard
| 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. |
Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining (2022.acl-long)
Copied to clipboard
| Challenge: | a model trained to align only two languages can encode multilingually more aligned representations . a dual-pivot transfer theory is proposed for bilingual training . |
| Approach: | They propose methods for learning cross-lingual sentence representations using paired or unpaired bilingual texts. |
| Outcome: | The proposed models reach the state of the art in unsupervised bitext mining and perform better than multilingually supervised models. |
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work uses sentences within the same batch as negatives, which suffers from easy negatives. |
| Approach: | They propose to align sentence representations from different languages into a unified embedding space . they adapt MoCo to further improve the quality of alignment . |
| Outcome: | The proposed model achieves state-of-the-art on several tasks. |
Bilingual Lexicon Induction through Unsupervised Machine Translation (P19-1)
Copied to clipboard
| 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. |
Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms (C18-1)
Copied to clipboard
| Challenge: | Existing methods for extracting bilingual terminology from comparable corpora are limited to a set of syntactic patterns. |
| Approach: | They propose a framework for aligning bilingual terms independently of term lengths . they introduce some enhancements to the context-based and neural network based approaches . |
| Outcome: | The proposed framework improves the performance of the context-based and neural network based approaches and can be adapted in specialized domains. |