Paying Attention to Multi-Word Expressions in Neural Machine Translation

Rikters, Matīss; Bojar, Ondřej

doi:10.48550/arxiv.1710.06313

Cited by 2 publications

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“…But since n-gramming does not provide context to phrases, this approach will be of limited help in logical reasoning (Bernardy and Chatzikyriakidis 2019;Zhou et al 2020). Instead, special techniques are needed for multiword expressions (Rikters and Bojar 2017) and also for proper names (e.g., transliteration from Latin to Cyrillic characters) (Petic and G fu 2014;Mansurov and Mansurov 2021).…”

Section: N-gramming: Computational Issuesmentioning

confidence: 99%

Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.

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Section: N-gramming: Computational Issuesmentioning

confidence: 99%

Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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“…Therefore, there is no explicit mechanism to attend to full phrases rather than subwords or characters. 16 Phrase-based NMT which equips the model with the ability to attend to full phrases or multi-word expressions has been studied by Rikters and Bojar [408], Ishiwatari et al [409], Feng et al [410], Huang et al [411], Li et al [412], Eriguchi et al [413].…”

Section: Advanced Attention Modelsmentioning

confidence: 99%

Neural Machine Translation: A Review and Survey

Stahlberg¹

2019

Preprint

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The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural machine translation (NMT), which tackles translation with a single neural network. In this work we will trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. We will conclude with a survey of recent trends in the field.

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Paying Attention to Multi-Word Expressions in Neural Machine Translation

Cited by 2 publications

References 0 publications

Comparison of text preprocessing methods

Comparison of text preprocessing methods

Neural Machine Translation: A Review and Survey

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