[PDF] Top 20 INMT: Interactive Neural Machine Translation Prediction
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INMT: Interactive Neural Machine Translation Prediction
... Machine Translation (MT) has witnessed several leaps of advancements and is now capable of pro- ducing human level translations (Läubli et ...human translation. Combining the pros of both human and ... See full document
6
Lightweight Word Level Confidence Estimation for Neural Interactive Translation Prediction
... the neural interactive translation prediction system recovers well from failure (predicting an incorrect token) when the correct token’s model score is also (rel- atively) ...similar ... See full document
6
Neural Machine Translation via Binary Code Prediction
... in neural machine translation ...bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 ... See full document
11
Learning to Stop in Structured Prediction for Neural Machine Translation
... BSO relies on unnormalized raw scores instead of locally-normalized probabilities to get rid of the label bias problem. However, since the raw score can be either positive or negative, the optimal stop- ping criteria ... See full document
6
Improving Japanese to English Neural Machine Translation by Voice Prediction
... NTCIR corpus. Herein, the highest accuracy is 66.0%, which is obtained by the model using three features, i.e., SrcSubj, SrcPred, and SrcVoice. Sr- cVoice is the best predictive feature because the voice concordance rate ... See full document
6
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... The quality of the translation also depends on the professions from the Occupations test and its predicted gender. Again, the system has no prob- lem predicting the gender of professions in the context of “him”, ... See full document
8
Adaptive Language and Translation Models for Interactive Machine Translation
... Cache-based language models were introduced by Kuhn and de Mori (1990) for the dynamic adap- tation of speech language models. These models, inspired by the memory caches on modern com- puter architectures, are motivated ... See full document
8
Distilling Knowledge for Search based Structured Prediction
... and neural machine translation experiments and plot the model’s per- formance on development sets in Figure ...the machine translation, the best α is ... See full document
10
Interactive Attention for Neural Machine Translation
... However, conventional attention model is conducted on the representation of source sentence (fixed af- ter generated) only with reading operation (Bahdanau et al., 2015; Luong et al., 2015a). This may let the decoder ... See full document
12
Interactive Predictive Neural Machine Translation through Reinforcement and Imitation
... presented neural interactive translation prediction — a translation scenario where transla- tors interact with an NMT system by accepting or correcting subsequent target tokens ... See full document
11
Active Learning for Interactive Neural Machine Translation of Data Streams
... on interactive NMT systems (Knowles and Koehn, 2016; Peris et ...an INMT system, for deciding whether the user should revise a partial hypothesis or ...of translation of unbounded data streams is ... See full document
10
Interactive Visualization and Manipulation of Attention based Neural Machine Translation
... While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behav- ...an interactive interface for visualizing and ... See full document
6
Variational Neural Machine Translation
... a translation example that helps un- derstand the advantage of VNMT over NMT ...the translation generated by Moses is relatively messy and ...by neural models (both GroundHog and VNMT) are much more ... See full document
10
Tensor2Tensor for Neural Machine Translation
... 1. Datasets: The Problem class encapsulate everything about a particular dataset. A Problem can generate the dataset from scratch, usually downloading data from a pub- lic source, building a vocabulary, and writing ... See full document
7
Scaling Neural Machine Translation
... of neural networks follows two main strategies: (i) model parallel evalu- ates different model layers on different work- ers (Coates et ...ral machine translation systems have been recently trained ... See full document
9
Boosting Neural Machine Translation
... ral Machine Translation (NMT), translation quali- ty has been improved significantly compared with traditional statistical based method (Bahdanau et ...the translation accept- able (Koehn and ... See full document
6
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
74
Iterative Back Translation for Neural Machine Translation
... Farsi translation is much more difficult than French; or a result of the diverse mix of domains in the parallel training data (news with LDC and technical talks with TED) where the domain in monolingual data is ... See full document
7
Combining Translation Memory with Neural Machine Translation
... plicated translation pairs between ...all translation pairs, which guarantees that these sentence-level translation pairs are inde- pendently sampled from the original ... See full document
8
Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... and Machine Translation (MT) make use of the knowledge and expertise of professional translators and interpreters in order to build and improve models for automatic translation or for developing more ... See full document
10
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