Papers with cross-lingual training
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains (2023.eacl-demo)
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| Challenge: | a lack of cross-lingual training data in emergent domains makes it difficult to train on emerging domains. |
| Approach: | They propose a cross-lingual open-retrieval question answering system for COVID-19 . their system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. |
| Outcome: | The proposed system outperforms BM25 baselines in cross-lingual settings. |
GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing (2022.aacl-short)
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| Challenge: | GCDT is the largest hierarchical discourse treebank for Mandarin Chinese in the framework of Rhetorical Structure Theory (RST). |
| Approach: | They propose to use a Chinese hierarchical discourse treebank to parse Mandarin Chinese using relation inventory and a multilingual training program. |
| Outcome: | The proposed dataset includes state-of-the-art scores for Chinese RST parsing and RST Parsing on the English GUM dataset, using cross-lingual training in Chinese and English with multilingual embeddings. |
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
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| 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. |
Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)
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| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)
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| Challenge: | Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers. |
| Approach: | They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task. |
| Outcome: | The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance. |