| Challenge: | Sentiment ambiguous lexicons are used when context is absent in translations . most systems aim to produce one correct translation for a given source sentence . |
| Approach: | They propose a neural machine translation method that preserves sentiment in two sentiment scenarios and a method that embeds sentiment into a sentence. |
| Outcome: | The proposed method outperforms a baseline with sentiment-aware translations in both the BLEU score and translation accuracy. |
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Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
| Approach: | They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent. |
| Outcome: | The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks. |
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing methods for neural machine translation only observe one source sentence at training time . this discrepancy in data distribution leads to a formidable learning challenge . |
| Approach: | They propose an uncertainty-aware semantic augmentation approach to capture universal semantic information among multiple source sentences and enhance hidden representations with this information. |
| Outcome: | The proposed approach outperforms baseline and existing methods on translation tasks. |
Machine Translation for Machines: the Sentiment Classification Use Case (D19-1)
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| Challenge: | Traditionally, machine translation (MT) pursues a "human-oriented" objective: generating fluent output for a downstream task. |
| Approach: | They propose a neural machine translation approach that uses weak feedback to generate translations that are best suited for a downstream task. |
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Handling Homographs in Neural Machine Translation (N18-1)
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| Challenge: | Existing methods for MT have problems with translating homographs, as it is difficult to select the correct translation based on the context. |
| Approach: | They propose to model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. |
| Outcome: | The proposed models improve translation accuracy and BLEU score on three language pairs. |
Data augmentation using back-translation for context-aware neural machine translation (D19-65)
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
| Approach: | They obtain large-scale pseudo parallel corpora by back-translating monolingual data and examine their impact on translation accuracy. |
| Outcome: | The large-scale pseudo parallel corpora obtained by back-translating monolingual data showed that the model trained with small parallel corporeals and large-sized pseudo parallels improved translation accuracy. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
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| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) struggles with the translation of rare multi-word expressions (MWEs). |
| Approach: | They propose a metric for automatically measuring the frequency of literal translation errors without human involvement. |
| Outcome: | The proposed metric measures the frequency of literal translation errors without human involvement with the models trained in different conditions and across a wide range of metrics and test sets. |
Addressing Troublesome Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words. |
| Approach: | They propose to use contextual memory to memorize which target words should be produced in which situations to translate troublesome words. |
| Outcome: | The proposed method outperforms baseline models on Chinese-to-English and English-to German translation tasks. |
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion (P19-1)
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| Challenge: | et al., 2018: translation errors due to the lack of extra-sentential context are becoming more and more noticeable among otherwise adequate translations. |
| Approach: | They propose a context-aware translation model that uses sentence-level data to identify inconsistencies . standard metrics are not sensitive to improvements in consistency in document-level translations . |
| Outcome: | The proposed model shows major gains over baseline without sacrificing performance . standard metrics are not sensitive to improvements in document-level translations . |