| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
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Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model (C18-1)
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| Challenge: | Incorporating syntactic information in machine translation can lead to better reorderings, especially useful when the language pairs are syntaktically highly divergent. |
| Approach: | They propose a forest-to-sequence NMT model which uses exponentially many parse trees of the source sentence to compensate for parser errors. |
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A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
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Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
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| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
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A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
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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 . |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
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On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
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