Papers with English-Tagalog
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)
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| Challenge: | a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely . |
| Approach: | They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair. |
| Outcome: | The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models . |
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations (P19-1)
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Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, William Hu, Neha Verma, Dragomir Radev
| Challenge: | Experimental results show that our model outperforms competitive translation-based baselines on cross-lingual relevance ranking tasks. |
| Approach: | They propose to match queries and documents in both source and target languages with deep bilingual query-document representations. |
| Outcome: | The proposed model outperforms translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual retrieval tasks. |