[PDF] Top 20 Models and Inference for Prefix Constrained Machine Translation
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Models and Inference for Prefix Constrained Machine Translation
... the prefix and translation of the ...domains: prefix, overlap and suffix. The prefix domain contains all phrases that are used for aligning the prefix with the source ...both ... See full document
10
Non-Autoregressive Machine Translation with Auxiliary Regularization
... neural machine translation approach, Non- Autoregressive machine Translation (NAT) has attracted at- tention recently due to its high efficiency in ...of translation, which causes NAT ... See full document
8
Domain Adaptive Inference for Neural Machine Translation
... Neural Machine Translation (NMT) models are ef- fective when trained on broad domains with large datasets, such as news translation (Bojar et ...general models can perform poorly (Koehn ... See full document
7
Towards Better Modeling Hierarchical Structure for Self Attention with Ordered Neurons
... on machine translation, targeted linguis- tic evaluation and logical inference tasks show that the proposed models achieve better performances by modeling hierarchical structure of ... See full document
6
Integer Linear Programming in NLP Constrained Conditional Models
... its inference engine, has recently attracted much attention within the NLP community, with multiple papers in all the recent major conferences, and a related workshop in ...as constrained optimization ... See full document
6
Bridging the Gap between Training and Inference for Neural Machine Translation
... In this paper, we present a method to bridge the gap between training and inference and improve the overcorrection recovery capability of NMT. Our method first selects oracle words from its pre- dicted words and ... See full document
10
Sparse and Constrained Attention for Neural Machine Translation
... Figure 2 shows examples of sentences for which the csparsemax fixed repetitions, along with the corresponding attention maps. We see that in the case of softmax repetitions, the decoder attends repeatedly to the same ... See full document
7
Recognizing Chinese Number and Quantifier Prefix to Enhance Statistical Parser in Machine Translation
... parsing models: a non-lexicalized, maximum likelihood- estimated PCFG (probabilistic context-free grammar) and a constituent-free dependency parsing, where semantics and syntactic structures were stored in ... See full document
8
Interactive Machine Translation using Hierarchical Translation Models
... Due to the demanding temporal constraints inher- ent to any interactive environment, performing a full search each time the user validates a new prefix is unfeasible. The usual approach is to rely on a certain ... See full document
11
Controlling Target Features in Neural Machine Translation via Prefix Constraints
... with prefix con- straints in terms of length control for IWSLT-2005 ...and Prefix Constraints stand for NMT sys- tems trained on the corpus with length tags placed at the end of source sentence and at the ... See full document
9
Speed Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
... Our approach is heavily based on the work of Gelbart et al. (2014) and Hern´andez-Lobato et al. (2015) which uses BO in the presence of unknown constraints. They set speed and memory constraints on neural network ... See full document
10
Distortion Models for Statistical Machine Translation
... Monotone decoding translates words in the same or- der they appear in the source language. Hence, the input and output sentences have the same word order. Monotone decoding is very efficient since the optimal decoding ... See full document
8
Learning to translate with products of novices: a suite of open ended challenge problems for teaching MT
... Machine translation (MT) draws from several different disciplines, making it a complex sub- ject to ...carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and ... See full document
14
Large Language Models in Machine Translation
... We use the MapReduce programming model (Dean and Ghemawat, 2004) to train on terabytes of data and to generate terabytes of language models. In this programming model, a user-specified map function processes an ... See full document
10
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers
... five translation tasks: Machine Translation of News, Machine Translation of IT domain, Biomedical Translation, Multimodal Machine Translation, and Cross-lingual ... See full document
28
Factored models for Deep Machine Translation
... model translation experience described in (Wang et ...Bulgarian-to-English translation was explored, in this paper also the English-to-Bulgarian direction is ... See full document
9
Probabilistic Inference for Machine Translation
... given translation string is calculated as the sum of the probabilities of all the derivations that yield that ...reference translation is not known, the exact calculation of this summa- tion is ...sample ... See full document
9
Constrained Grammatical Error Correction using Statistical Machine Translation
... Nitin Madnani, Joel Tetreault, and Martin Chodorow. 2012. Exploring grammatical error correction with not-so-crummy machine translation. In Proceedings of the Seventh Workshop on Building Educational ... See full document
10
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... Unsupervised NMT The settings of the word- level NMT and subword-level NMT are the same, except the vocabulary size. We use a vocabulary size of 50k in the word-level NMT setting and 40k in the subword-level NMT setting ... See full document
8
Urdu to English Machine Translation using Bilingual Evaluation Understudy
... for translation is much larger than one ...repetitive translation and improvements by human annotators and translators contribute significantly to any MT ... See full document
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