| Challenge: | Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking. |
| Approach: | They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments. |
| Outcome: | The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models. |
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
| Outcome: | The proposed approach outperforms the best CoNLL 2018 systems in all of the shared task’s six truly low-resource languages while using a single system. |
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing (N19-1)
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| Challenge: | Existing methods for multilingual transfer are limited by their dynamic nature. |
| Approach: | They propose a method that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. |
| Outcome: | The proposed method outperforms the state-of-the-art on 6 languages, yielding an improvement of 6.8 LAS points on average. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
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Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
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Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (D19-1)
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| Challenge: | Pretrained contextual representation models have pushed forward the state-of-the-art on many NLP tasks. |
| Approach: | They propose to use a model that is pretrained on 104 languages for cross-lingual transfer. |
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How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
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A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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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 . |
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Language-agnostic BERT Sentence Embedding (2022.acl-long)
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| Challenge: | Existing methods for learning bilingual sentence embeddings are not well explored. |
| Approach: | They propose to combine best methods for learning multilingual sentence embeddings with pre-trained models to achieve 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba. |
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On the Cross-lingual Transferability of Monolingual Representations (2020.acl-main)
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| Challenge: | State-of-the-art unsupervised multilingual models generalize in zero-shot cross-lingual setting . generalization ability attributed to shared subword vocabulary and joint training across multiple languages . |
| Approach: | They propose an approach that transfers a monolingual model to new languages at the lexical level. |
| Outcome: | The proposed approach is competitive with multilingual BERT on cross-lingual classification benchmarks and on a new cross-linguistic question answering dataset. |
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)
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| 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. |
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