[PDF] Top 20 Linguistic Input Features Improve Neural Machine Translation
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Linguistic Input Features Improve Neural Machine Translation
... Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic in- ...of neural MT models does not make linguistic fea- ... See full document
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Improving Machine Translation Quality Estimation with Neural Network Features
... to improve the correlation between the au- tomatic QE and human ...motivated features requires part-of-speech analysis, syntactic analysis, or se- mantic analysis, and these linguistic analyses re- ... See full document
5
Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... Machine translation, which aims to perform transition between distinct languages, is a major focus of NLP research ...source-side linguistic information as prior knowledge to improve ... See full document
9
Quality Estimation with Force Decoded Attention and Cross lingual Embeddings
... a neural machine translation system and cross-lingual phrase embeddings as input features of a re- gression ...a neural machine translation sys- tem with an ... See full document
6
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
... Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy ... See full document
8
Compositional Representation of Morphologically Rich Input for Neural Machine Translation
... taining translation at the lexical level apparently aids the attention mechanism and provides more semantically and syntactically consistent transla- ...consistent improve- ments across all languages, our ... See full document
7
Multimodal Neural Machine Translation for Low resource Language Pairs using Synthetic Data
... ral machine translation (NMT) and image de- scription generation (IDG) that explicitly uses an encoder-decoder framework as an instan- tiation of the sequence to sequence (seq2seq) learning problem (Cho et ... See full document
10
Ensembling Factored Neural Machine Translation Models for Automatic Post Editing and Quality Estimation
... specialized Neural Machine Translation (NMT) ...Word-level features that have proven effective for QE are included as input factors, expanding the representation of the original source ... See full document
8
Exponentially Decaying Bag of Words Input Features for Feed Forward Neural Network in Statistical Machine Translation
... zero to one. It specifies how fast weights of contextual words decay along with dis- tances, which can be learned like other weight parameters of the neural network. Instead of using fixed decay rate as in (Irie ... See full document
6
Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... We evaluated models that are trained both on the translation and POS tagging task. Although the POS data is out-of-domain and significantly smaller than the parallel training data for the trans- lation task (ca. ... See full document
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Tree Kernels for Machine Translation Quality Estimation
... They improve over the baseline performance in two ways, building on and extend- ing earlier work by Hardmeier (2011), on which the system description in the following sections is partly based: On the one hand, we ... See full document
5
Controlling Target Features in Neural Machine Translation via Prefix Constraints
... cific linguistic phenomena in one language, such as zero pronouns (dropped subject and object) in Japanese and expletives in English (there in there- construction, do in interrogative sentence, it in for- mal ... See full document
9
Incorporating Global Visual Features into Attention based Neural Machine Translation
... purely neural architecture could improve on the Phrase-Based SMT (PB- SMT) ...visual features in re- ranking n-best lists generated by a PBSMT sys- tem or directly in a purely NMT framework with some ... See full document
12
Document Level Information as Side Constraints for Improved Neural Patent Translation
... and neural translation models for patent ...phrase-based machine translation, our features based on annotation overlap between test documents and phrase context were not ...For ... See full document
12
Error Detection for Statistical Machine Translation Using Linguistic Features
... to improve machine translation ...system-based features, such as word posterior probabilities calculated from N- best lists or word ...of linguistic fea- tures, which convey information ... See full document
8
NAVER Machine Translation System for WAT 2015
... Neural machine translation (NMT) is a new ap- proach to machine translation that has shown promising results compared to the existing ap- proaches such as phrase-based statistical ... See full document
5
Beyond Weight Tying: Learning Joint Input Output Embeddings for Neural Machine Translation
... the input em- beddings with those of the output classifiers (Press and Wolf, 2017; Inan et ...and machine transla- tion (Sennrich et ...similar input embeddings to have a similar chance to be ... See full document
11
Boosting Neural Machine Translation
... Anabel Rebollo, Kathy Yang, Jean Senellart, E- gor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean ... See full document
6
Combining Translation Memory with Neural Machine Translation
... and Neural Machine Transla- tion (NMT) models, where we can select fi- nal translation outputs from either a translation memory or an NMT system, when the similar- ity score of a test source ... See full document
8
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
... This talk describes our recent work on developing unsupervised speech technology, where transcripts and pronunciation dictionaries are not used. The work is inspired by considering both how young infants may begin to ... See full document
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