[PDF] Top 20 A discriminative language model with pseudo negative samples
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A discriminative language model with pseudo negative samples
... Another problem for DLMs is that the number of features becomes very large, because all possible N- grams are used as features. In particular, the mem- ory requirement becomes a serious problem because quite a few active ... See full document
8
Large scale discriminative language model reranking for voice search
... voice-search! Discriminative language models (DLMs) attempt to directly optimize error rate by rewarding features that appear in low error hypothe- ses and penalizing features in misrecognized hy- ...large ... See full document
9
Refining Generative Language Models using Discriminative Learning
... negative samples. Given that the corpora used for training language models contain only real sentences, ...positive samples, obtaining these can be ...on discriminative language ... See full document
8
Online non negative discriminative dictionary learning for tracking
... training samples of the target template dictionaries are increasing, and the dictionaries are required to maintain a high update ...tracking model which used a semi-supervised appearance dictio- nary ... See full document
12
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
... DPR model: in English the phrase “any of” usually stays in front of the subjects (or objects) it ...training samples a discriminative model such as DPR is able to capture various grammatical ... See full document
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Discriminative Reranking for Natural Language Parsing
... the model will be optimal in the sense of convergence to the true underlying distribution generating ...a model with a smaller number of parameters will require fewer samples for convergence, but ... See full document
46
Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm
... existing language model, but rather com- plement it. The existing language model has the benefit that it can be trained on a large amount of text that does not have speech ...a ... See full document
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Discriminative Pruning of Language Models for Chinese Word Segmentation
... As shown in equation (13), the "importance" of each bigram depends on the base model. Ini- tially, the base model is set to the unigram model. With bigrams added in, it becomes a growing ... See full document
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Discriminative Language Models as a Tool for Machine Translation Error Analysis
... regularized discriminative LMs to solve the above problem. Discriminative LMs are LMs trained to fix common output errors of a particular ...a discriminative LM using n-gram features and examine the ... See full document
9
Multi modal Discriminative Model for Vision and Language Navigation
... helps model performance across the ...the model with a good initial policy for further fine-tuning on tougher negatives pat- terns in PR and RW ...the model is first trained on only PS negatives and ... See full document
10
A Generative Parser with a Discriminative Recognition Algorithm
... We performed experiments on the English Penn Treebank dataset; we used sections 2–21 for train- ing, 24 for validation, and 23 for testing. Follow- ing Dyer et al. (2015), we represent each word in three ways: as a ... See full document
7
Discriminative Approach to Fill in the Blank Quiz Generation for Language Learners
... Table 3 shows the results of the first experiment; RAD with a 95% confidence interval and inter- rater agreement κ. All of our proposed methods outperform baselines regarding RAD with high inter-rater agreement. In ... See full document
5
A Discriminative Model to Predict Comorbidities in ASD
... For this application, the regular expression greedily matched on all posts indicative of age, pattern matching on phrases like ”six-year-old” and ”is six years.” The patterns were designed to incorporate specific uses of ... See full document
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A Discriminative Model for Semantics to String Translation
... Next, we ran one iteration of the MERT opti- mizer on these 1000-best lists for all of the fea- tures. Because this was a reranking experiment rather than decoding, we did not repeatedly gather n-best lists as in ... See full document
7
Confidence Weighted Learning of Factored Discriminative Language Models
... Language models based on word surface forms only are unable to benefit from avail- able linguistic knowledge, and tend to suffer from poor estimates for rare features. We pro- pose an approach to overcome these ... See full document
6
Similar Document Retrieval using Pattern-Based Topic Modelling for Information Filtering
... The user interest modelling is a process to understand the user’s information needs based on the most relevant information that can be found and delivered to the user. In order to extract precise user’s interests, ... See full document
5
University of Strathclyde at TREC HARD
... the Discriminative and Representative queries (STRAxqedt and STRAxqert respec- tively), we submitted one run each that expanded the original query with either the top sixth ranked Dis- criminative or ... See full document
11
A Discriminative Alignment Model for Abbreviation Recognition
... a discriminative align- ment model for extracting abbreviations and their full forms appearing in actual ...alignment model and corpus for improving abbrevia- tion ... See full document
8
A Discriminative Model for Polyphonic Piano Transcription
... by Marolt [8]. This set consists of six recordings from the same piano and recording conditions used to train his neu- ral net and is different from any of the data in our train- ing set. The results of this test are ... See full document
9
Towards Developing Generation Algorithms for Text to Text Applications
... trigram language model using Kneser-Ney smoothing, on 10 million sentences (170 million words) from the Wall Street Journal (WSJ), lower case and no final ... See full document
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