Zero-shot Cross-lingual Alignment for Embedding Initialization (2024.findings-acl)
Copied to clipboard
| Challenge: | CrossInit initializes embeddings into similar geometrical structures across languages in unsupervised manner. |
| Approach: | They propose a method that initializes embeddings into similar geometrical structures across languages in an unsupervised manner. |
| Outcome: | The proposed method demostrates similar patterns in low-resource and dissimilar languages. |
Similar Papers
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
| Approach: | They propose an unsupervised initialization method that exploits structural similarity of embeddings and a robust self-learning algorithm that iteratively improves it. |
| Outcome: | The proposed method achieves the best published results in standard datasets even surpassing previous supervised systems. |
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. |
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for word embeddings are limited to a single, unannotated corpus, which means that word representations with similar meaning in distinct languages can be very different. |
| Approach: | They propose an unsupervised method for cross-lingual word embedding mapping that uses stochastic initialization and isometric initialization to verify the method's robustness. |
| Outcome: | The proposed method is robust on different embedding representations and new language pairs, particularly those involving Slavic languages like Polish or Czech. |
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages (2024.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models are weak in addressing cross-lingual transfer tasks. |
| Approach: | They propose a method for initializing embeddings and choosing the right vocabulary size for cross-lingual systems. |
| Outcome: | The proposed method improves the F1-Score in several languages . |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
Copied to clipboard
Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent studies have reported that domain mismatch prevents cross-lingual transfer . UBLI and UNMT do not work well when underlying monolingual corpora come from different domains . |
| Approach: | They show that a simple initialization regimen can overcome domain mismatch in cross-lingual transfer . they pre-train word embeddings on concatenated domain-mismatched corpora and use them as initializations . |
| Outcome: | The initialization regimen can overcome the domain mismatch effect in cross-lingual transfer learning . the initializations were used for MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 task . |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
Unsupervised Cross-Lingual Representation Learning (P19-4)
Copied to clipboard
| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Hierarchical Mapping for Crosslingual Word Embedding Alignment (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing strategies that map word embeddings into a crosslingual space are biased towards the choice of the pivot language. |
| Approach: | They propose to map any two languages into a different middle space by learning mappings across languages in a hierarchical way. |
| Outcome: | The proposed strategy significantly improves vocabulary induction scores in all existing benchmarks and in a new non-English–centered benchmark. |
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality. |
| Approach: | They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model. |
| Outcome: | The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs. |