Multi-Granularity Contrasting for Cross-Lingual Pre-Training (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to pre-training focus on embedding alignment, but they neglect the modeling of bidirectional contexts. |
| Approach: | They propose a framework to learn languageuniversal representations using multi-granularity contrasting framework . they encode semantic equivalents from different languages into similar representations . |
| Outcome: | The proposed framework can achieve significant performance gains in machine translation and cross-lingual language understanding. |
Similar Papers
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
Copied to clipboard
Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| Challenge: | Pre-trained language models have been shown to improve performance in many natural language tasks. |
| Approach: | They propose to combine cross-lingual and visual pre-training to learn visually-grounded cross-linguistic representations using masked region classification and three-way parallel vision & language corpora. |
| Outcome: | The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation. |
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. |
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)
Copied to clipboard
Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou
| Challenge: | Existing methods for learning cross-lingual representations are lacking in the field of NLP. |
| Approach: | They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. |
| Outcome: | The proposed approach improves cross-lingual transferability on benchmarks. |
Multilingual Pre-training with Self-supervision from Global Co-occurrence Information (2023.findings-acl)
Copied to clipboard
| Challenge: | Empirical studies show multilinguality and crosslinguality emerge from MLM pretraining without supervision. |
| Approach: | They propose to use global co-occurrence information as a source of structural information on multilingual corpora. |
| Outcome: | Empirical studies show that MLM-GC pre-training outperforms MLM pre- training for 4 downstream cross-lingual tasks and 1 additional monolingual task. |
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. |
Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. |
| Approach: | They propose a co-tuning method that aims to learn more generalized semantic equivalences when the languages are structurally dissimilar. |
| Outcome: | The proposed method improves on cross-lingual inference and review tasks by capturing the semantic relationship in the parallel data when a few translation pairs are available. |
InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs. |
| Approach: | They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models. |
| Outcome: | The proposed approach improves the performance of multilingual language models on low-resource languages. |
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. |
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding (2022.emnlp-main)
Copied to clipboard
Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad Morariu
| Challenge: | Existing methods learn features from word-level or region-level but fail to consider both simultaneously. |
| Approach: | They propose a multi-modal multi-granular pre-training framework that encodes page-level, region-level and word-level information at the same time. |
| Outcome: | The proposed model learns features from word-level and region-level but fails to consider both simultaneously. |
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. |
| Approach: | They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives. |
| Outcome: | The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets. |