Papers with WMT datasets
Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)
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| Challenge: | Multilingual neural machine translation models suffer from performance degradation when learning multiple languages. |
| Approach: | They propose to use LaSS to jointly train a single unified multilingual MT model. |
| Outcome: | The proposed model gains on 36 language pairs by up to 1.2 BLEU and zero-shot translation with 8.3 BLUE on 30 language pairs. |
Gradient-based Gradual Pruning for Language-Specific Multilingual Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Multilingual neural machine translation suffers from performance degradation in high-resource languages compared to bilingual counterparts. |
| Approach: | They propose a gradient-based gradual pruning technique for multilingual neural machine translation that allows for partial parameter sharing across language pairs to alleviate interference. |
| Outcome: | The proposed approach yields a notable performance gain on IWSLT and WMT datasets. |
Multi-task Learning for Multilingual Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing multilingual neural machine translation systems rely on bitext training data, which is limited and costly to collect. |
| Approach: | They propose a multi-task learning framework that trains the model with the translation task on bitext data and two denoising tasks on monolingual data. |
| Outcome: | The proposed framework outperforms pre-training models for both NMT and cross-lingual transfer learning NLU tasks. |
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation (2020.acl-main)
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| Challenge: | Empirical results on machine translation suggest that DPE is effective for segmenting output sentences. |
| Approach: | They propose a new algorithm for tokenizing sentences into subword units . they propose enabling exact log marginal likelihood estimation and exact MAP inference . |
| Outcome: | The proposed algorithm improves on machine translation datasets and on a large dataset. |
Synchronously Generating Two Languages with Interactive Decoding (D19-1)
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| Challenge: | Experimental results show that multilingual NMT models handle multiple language pairs in one model. |
| Approach: | They propose an interactive approach to translate a source language into two different languages simultaneously and interactively. |
| Outcome: | The proposed approach improves on IWSLT and WMT datasets. |
Isotropy-Enhanced Conditional Masked Language Models (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models with auto-regressing decoding paradigms have been used for various text generation tasks to accelerate inference but at the cost of generation quality to some extent. |
| Approach: | They propose to use Look Neighbors strategy to enhance learning of target token representations during training to achieve a good balance between inference speedup and generation quality. |
| Outcome: | The proposed models outperform current models on 4 WMT datasets and outperformed the current SoTA results. |