An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization (C18-1)
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| Challenge: | In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages . we find that NMT model is much better than SMT in terms of character error rate . |
| Approach: | They propose to use NMT models to solve the problem of historical spelling normalization in five languages. |
| Outcome: | The proposed method improves historical spelling normalization for five languages. |
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| Challenge: | Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules. |
| Approach: | They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena . |
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Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
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One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
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On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
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On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
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On Compositional Generalization of Neural Machine Translation (2021.acl-long)
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| Challenge: | Modern neural machine translation models have shown competitive performance in benchmarks such as WMT, but there are significant issues such as robustness, domain generalization, etc. |
| Approach: | They propose a benchmark dataset for NMT models from the perspective of compositional generalization and quantitatively analyze the results. |
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Fast and Accurate Neural Machine Translation with Translation Memory (2021.acl-long)
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| Challenge: | Existing knowledge demonstrates the superiority of TM-based neural machine translation only on TM specialized tasks . |
| Approach: | They propose a translation memory-based approach to machine translation using a single bilingual sentence as its TM. |
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Revisiting Negation in Neural Machine Translation (2021.tacl-1)
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| Challenge: | Negation is an important linguistic phenomenon in machine translation, as errors in translating negation may change the meaning of source sentences completely. |
| Approach: | They evaluate the translation of negation in English–German (EN–DE) and English– Chinese (EN-ZH) . they find that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation . |
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English-Basque Statistical and Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) requires large training corpora, which is problematic for low-resource languages. |
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On the Importance of Word Boundaries in Character-level Neural Machine Translation (D19-56)
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| Challenge: | Neural Machine Translation models typically use a fixed-size lexical vocabulary . subword segmentation methods rely on statistical heuristics that lack any linguistic notion . |
| Approach: | They propose a hierarchical decoding architecture for character-level NMT using subwords . they propose fewer parameters and a more efficient approach to perform translation at the level of words . |
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