Papers with cross-lingual language understanding
Translation Errors Significantly Impact Low-Resource Languages in Cross-Lingual Learning (2024.eacl-short)
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| Challenge: | XNLI benchmarks use parallel versions of English evaluation sets in multiple target languages . a recent study found that translation errors exist in some low-resource languages resulting in incorrect estimates of cross-lingual transfer . |
| Approach: | They propose to measure the gap in performance between zero-shot evaluations on human-translated and machine-transcribed target text across multiple target languages. |
| Outcome: | The proposed benchmarks show that translation errors exist for Hindi and Urdu . the results corroborate previous studies that found translation errors in Hindi and urdu despite translation errors. |
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | X-STA is a new approach for cross-lingual machine reading comprehension . the variation of answer span positions in different languages makes it difficult to transfer knowledge across languages. |
| Approach: | They propose a method that leverages an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on three multi-lingual datasets. |
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual natural language inference lack annotated parallel corpora. |
| Approach: | They propose a new prompt learning framework with the Multilingual Verbalizer for XNLI that uses a multilingual verbalizer to align the representations of original and augmented multilingual questions into a unified semantic space with consistency regularization. |
| Outcome: | The proposed framework outperforms existing methods under few-shot and full-shot cross-lingual transfer settings. |
Multi-Granularity Contrasting for Cross-Lingual Pre-Training (2021.findings-acl)
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| 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. |
XNLI: Evaluating Cross-lingual Sentence Representations (D18-1)
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Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov
| Challenge: | State-of-the-art natural language processing systems rely on annotated data to learn competent models. |
| Approach: | They extend the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including Swahili and Urdu. |
| Outcome: | The proposed evaluation set extends the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages including low-resource languages such as Swahili and Urdu. |
ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing models that use multilingual captions for images have limited results due to the scarcity of training data. |
| Approach: | They propose a multilingual vision-and-language model that divides a V&L task into two stages . they propose IC, which takes the caption as the alt text and performs cross-lingual language understanding . |
| Outcome: | The proposed model can achieve state-of-the-art results for five languages and comparable results for the rest. |