[PDF] Top 20 Probabilistic Inference for Machine Translation
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Probabilistic Inference for Machine Translation
... includes all the derivations that fall within the cube pruning beam, hopefully representing the majority of the probability mass. We denote the partition function estimated with this cube beam approxima- tion as Z ˜ Λ cb ... See full document
9
Consistency by Agreement in Zero Shot Neural Machine Translation
... multilingual translation often highly depend on the amount of available parallel data for each language pair of ...on translation directions they have not been optimized for at training ...tilingual ... See full document
14
A joint inference of deep case analysis and zero subject generation for Japanese to English statistical machine translation
... 3.2 Joint inference with linguistic constraints Our initial model (2) assumes that zero subjects and deep cases are generated independently. How- ever, this assumption does not always capture real linguistic ... See full document
6
A Markov Model of Machine Translation using Non parametric Bayesian Inference
... As mentioned above, the hierarchical PYP takes into consideration a rich history to evaluate the probabilities of translation decisions. But this leads to difficulties when applying the model to large data sets, ... See full document
10
A Neural Attention Model for Abstractive Sentence Summarization
... We have presented a neural attention-based model for abstractive summarization, based on recent de- velopments in neural machine translation. We combine this probabilistic model with a genera- tion ... See full document
11
GREAT: A Finite State Machine Translation Toolkit Implementing a Grammatical Inference Approach for Transducer Inference (GIATI)
... GREAT is a finite-state toolkit which was born to overcome the computational problems that pre- vious implementations of GIATI (Picó, 2005) had in practice when huge amounts of data were used. Even more, GREAT is the ... See full document
9
Fast Neural Machine Translation Implementation
... Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz Univer- sity, Tilde and University of ...fast inference en- gine for neural machine ... See full document
6
Greedy Search with Probabilistic N gram Matching for Neural Machine Translation
... the translation properly and suffers from the exposure bias, and sequence-level objectives are usually indifferen- tiable and require gradient ...propose probabilistic sequence-level objectives based on n- ... See full document
7
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
... Table 1 shows results of NLI classifiers trained on representations from different NMT encoders. We also report the majority baseline and the results of Bowman et al.’s 3-layer deep 200 dimensional neural network used by ... See full document
11
Probabilistic Finite State Machines for Regression based MT Evaluation
... automatic machine translation (MT) eval- uation metrics has been a key driving force behind the recent advances of statistical machine transla- tion (SMT) ... See full document
11
Survey on Attention Neural Network Models for Natural Language Processing
... like machine translation, sentence summarization,sentence pair modeling, paraphrase identification, natural language inference, question answering etc typically learn the sentence embedding for ... See full document
5
Contextual Text Denoising with Masked Language Model
... We test the performance of the proposed text de- noising method on three downstream tasks: neural machine translation, natural language inference, and paraphrase detection. All experiments are ... See full document
5
Learning Probabilistic Synchronous CFGs for Phrase Based Translation
... statistical machine transla- tion because they can express reordering phenomena between pairs of ...hierarchical, probabilistic devices from parallel corpora constitutes a major challenge, because of ... See full document
9
On the Hardness of Probabilistic Inference Relaxations
... and probabilistic inference are computationally hard (Valiant 1979; Roth ...tic inference, and various approximations and relaxations are used in practice ...exact inference, while being ... See full document
8
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
A Probabilistic Approach to Syntax based Reordering for Statistical Machine Translation
... iment setting, the best distortion limit for Chinese- English translation is 4. However, some ideal trans- lations exhibit reorderings longer than such distor- tion limit. Consider the sentence pair in NIST MT- ... See full document
8
Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars
... The training set consists of Xinhua newswire data from LDC and the FBIS data (mostly news), both filtered to ensure parallel sentence pair quality. We used the development test data from the 2001 NIST MT evaluation ... 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 ... See full document
7
Models and Inference for Prefix Constrained Machine Translation
... interactive machine trans- lation: suggesting how to complete a par- tial ...and inference algorithms tailored to this ...neural translation system in- creases accuracy yet further to ...but ... See full document
10
Bridging the Gap between Training and Inference for Neural Machine Translation
... at inference. To mitigate the discrepancy be- tween training and inference, when predicting one word, we feed as context either the ground truth word or the previous predicted word with a sam- pling ...real ... See full document
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