Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
Similar Papers
Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
| Approach: | They propose to use multilingual masked language modeling to train a model on concatenated text from multiple languages to find universal latent symmetries in embedding spaces. |
| Outcome: | The proposed models can be trained on concatenated text from multiple languages without shared vocabulary or domain similarity. |
Mask-Align: Self-Supervised Neural Word Alignment (2021.acl-long)
Copied to clipboard
| Challenge: | Word alignment is an important task in many natural language processing tasks. |
| Approach: | They propose a self-supervised word alignment model that takes advantage of the full context on the target side. |
| Outcome: | The proposed model outperforms previous unsupervised models and obtains state-of-the-art results on four language pairs. |
PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models exhibit reasonable multilingual abilities, despite predominantly English-centric pretraining. |
| Approach: | They propose a framework that establishes multilingual alignment prior to language model pretraining and preserves this alignment using a code-switching strategy during pretraining. |
| Outcome: | Experiments in a synthetic English to English-Clone setting show that PreAlign outperforms standard multilingual joint training in language modeling, zero-shot cross-lingual transfer, and cross-linguistic knowledge application. |
Take a Closer Look at Multilinguality! Improve Multilingual Pre-Training Using Monolingual Corpora Only (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have demonstrated remarkable cross-lingual capability of pre-trained language models . however, semantic alignments may be the reason behind such capability but remain under-explored. |
| Approach: | They propose token-level and semantic-level code-switched masked language modeling to improve cross-lingual interactions over mono-mPLMs without parallel sentences. |
| Outcome: | The proposed method outperforms mono-mPLMs on natural language understanding and unsupervised machine translation tasks. |
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English. |
| Approach: | They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models. |
| Outcome: | The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters. |
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)
Copied to clipboard
Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. |
| Approach: | They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment. |
| Outcome: | The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks. |
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. |
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks. |
| Approach: | They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages. |
| Outcome: | The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |
Curriculum Masking in Vision-Language Pretraining to Maximize Cross Modal Interaction (2024.naacl-long)
Copied to clipboard
| Challenge: | masked language modeling is widely used as a pretraining component in Vision and language (V+L) but performance on benchmarks has not received the attention it deserves. |
| Approach: | They propose a curriculum masking scheme that uses a parallel mask selection agent to mask tokens at a frequency proportional to the level of cross modal interaction necessary to reconstruct them. |
| Outcome: | The proposed method improves relational understanding on a wide range of V+L tasks. |