Explicit Alignment Objectives for Multilingual Bidirectional Encoders (2021.naacl-main)
Copied to clipboard
| Challenge: | Pre-trained cross-lingual encoders have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resourced languages. |
| Approach: | They propose a method to align multilingual encoders using two explicit alignment objectives that align the multilingual representations at different granularities. |
| Outcome: | The proposed method achieves gains of up to 1.1 average F1 score on sequence tagging and 27.3 average accuracy on retrieval over the XLM-R-large model. |
Similar Papers
Do Explicit Alignments Robustly Improve Multilingual Encoders? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Explicit alignment objectives based on bitexts like Europarl and MultiUN have been shown to improve cross-lingual representations. |
| Approach: | They propose a new contrastive alignment objective that can better utilize bitexts . they propose to use a random sample of 1 million pair subset of OPUS data . |
| Outcome: | The proposed objective outperforms existing alignment objectives on a random 1 million pair subset of the OPUS dataset. |
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 Multilingual Embeddings for Improved Code-switched Natural Language Understanding (2022.coling-1)
Copied to clipboard
| Challenge: | a recent study has shown that multilingual models can be effective on monolingual data but need additional training to work well with code-switched text. |
| Approach: | They propose to train multilingual models with alignment objectives using parallel text . they find such an explicit alignment step improves performance on code-switched NLP tasks . |
| Outcome: | The proposed model improves on Hindi-English Sentiment Analysis, Named Entity Recognition and Question Answering tasks. |
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 . |
XeroAlign: Zero-shot cross-lingual transformer alignment (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing zero-shot approaches to improve multilingual NLP tasks require translation of task data to bridge the gap between the source and target language. |
| Approach: | They propose a method for task-specific alignment of cross-lingual pretrained transformers such as XeroAlign that uses translated task data to encourage the model to generate similar sentence embeddings for different languages. |
| Outcome: | The proposed method performs on par with state-of-the-art models on a cross-lingual adversarial paraphrasing task and its text classification accuracy exceeds that of XLM-R trained with labelled data. |
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. |
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies show that multilingual generative models exhibit a strong language bias toward high-resource languages. |
| Approach: | They propose a cross-lingual alignment framework exploiting pairs of translation sentences to improve cross-linguistic abilities. |
| Outcome: | The proposed framework improves cross-lingual abilities and mitigates performance gap. |
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (2023.findings-acl)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) graphs embed the semantics of a sentence in a directed acyclic graph, where concepts are represented by nodes, semantic relations between concepts by edges, and the co-references by reentrant nodes. |
| Approach: | They propose a novel aligner for Abstract Meaning Representation graphs that scales cross-lingually and can align units and spans in sentences of different languages. |
| Outcome: | The proposed aligner achieves state-of-the-art in the benchmarks and can scale cross-lingually. |
Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent mPLMs have shown impressive performance on crosslingual transfer tasks . however, the performance is often hindered when a lowresource target language is written in a different script than the high-resource source language. |
| Approach: | They propose a transliteration-based method to improve cross-lingual alignment between languages using diverse scripts. |
| Outcome: | The proposed method outperforms the original model on Englishcentric transfer tasks up to 50%. |
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
Copied to clipboard
Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |