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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 421–430 of 1773 papers

TitleStatusHype
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Direct Neural Machine Translation with Task-level Mixture of Experts models—0
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language—0
A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation—0
Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation—0
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval—0
Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach—0
Constraining the Transformer NMT Model with Heuristic Grid Beam Search—0
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