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 . |
Similar Papers
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. |
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. |
Unsupervised Bilingual Lexicon Induction via Latent Variable Models (D18-1)
Copied to clipboard
| Challenge: | Existing methods for generating bilingual lexicons use parallel corpora or bilingual dictionaries. |
| Approach: | They propose a model which builds cross-lingual dictionaries using latent variable models and adversarial training with no parallel corpora. |
| Outcome: | The proposed model outperforms state-of-the-art models on several language pairs and reaches competitive performance. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
Copied to clipboard
| 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. |
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction (2022.emnlp-main)
Copied to clipboard
Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, Xing Xie
| Challenge: | Existing approaches focus on minimizing distances between words in aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. |
| Approach: | They propose a ranking-oriented induction model to learn personalized mapping function for each word. |
| Outcome: | The proposed model can learn personalized mapping function for each word on public datasets including rich-resource and low-resourced languages. |
Orthographic Features for Bilingual Lexicon Induction (P18-2)
Copied to clipboard
| Challenge: | Recent embedding-based methods do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. |
| Approach: | They propose to use orthographic features to integrate orthographic induction into embedding methods . they use document-aligned data instead of a seed dictionary to learn bilingual embedds . |
| Outcome: | This work extends embedding-based methods to incorporate orthographic features . it shows that the methods can learn bilingual embeddables in low-resource languages . |
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 for Low-Resource Languages using Graph Matching via Optimal Transport (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing literature on bilingual lexicon induction fails in low-resource scenarios . a language dataset is considered low- resource based on its own embedding space . |
| Approach: | They propose a graph-matching method that improves bilingual lexicon induction performance across 40 language pairs using optimal transport. |
| Outcome: | The proposed method is especially strong with low amounts of supervision. |
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space (2020.emnlp-main)
Copied to clipboard
| 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. |
Evaluating bilingual word embeddings on the long tail (N18-2)
Copied to clipboard
| Challenge: | Bilingual word embeddings are useful for bilingual lexicon induction, but they focus on frequent words in general domains. |
| Approach: | They propose to evaluate bilingual word embeddings on rare words in different domains . they propose to use a multilingual dataset to build and combine BWEs based on a single word . |
| Outcome: | The proposed evaluations show that state-of-the-art methods fail on rare words . the proposed evaluation is based on a gold standard dataset and code . |