[PDF] Top 20 From n gram based to CRF based Translation Models
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From n gram based to CRF based Translation Models
... any n-gram model, the parameters are es- timated using statistics collected in a training corpus made of sequences of tuples derived from the par- allel sentences in a two step ...1 based on ... See full document
12
Automatic Grammatical Error Detection for Chinese based on Conditional Random Field
... In the aspect of automatic detection of grammatical errors, the study of English is more deep. Anubhav Gupta (2014) proposed a rule-based approach that relies on the difference in the output of two POS taggers, to ... See full document
6
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Third, CONV42 was better than BNLM42 for both first-pass and reranking. This also holds in the case of CONV746 and BNLM746. This indicated that our conversion method improved the BNLMs, even if the underlying BNLM was ... See full document
6
Letter N Gram based Input Encoding for Continuous Space Language Models
... scripts from the Moses package de- scribed in Koehn et al. (2007). A 4-gram language model was trained on the target side of the parallel data using the SRILM toolkit from Stolcke ...the ... See full document
10
Data Driven Response Generation in Social Media
... response-generation models, we use a corpus of roughly 1.3 million conversations scraped from Twitter (Ritter et ...reference from each reply to the post it responds to, so unlike IRC, there is no ... See full document
11
Dependency Based N Gram Models for General Purpose Sentence Realisation
... Table 3: Examples of lexical macros The input to our generator are unordered f- structures automatically derived from the develop- ment and test set trees of our treebanks, which do not contain any string position ... See full document
8
LeBLEU: N gram based Translation Evaluation Score for Morphologically Complex Languages
... same n-gram or zero points for any difference, we include “soft” or “fuzzy” hits for word n-grams based on letter edit ...parameters, n-gram length and fuzzy match threshold, ... See full document
6
The Operation Sequence Model—Combining N Gram Based and Phrase Based Statistical Machine Translation
... and N-gram-based (Ncode) systems on three standard tasks of translating German-to-English, Spanish-to-English, and ...minimal translation units during decoding makes the search problem ... See full document
30
Truly Exploring Multiple References for Machine Translation Evaluation
... of n-grams in string matching evaluation metrics, none of which take full advantage of the recur- ring information in these ...the n-gram dis- tribution and on divergences in multiple references, we ... See full document
8
Smooth Bilingual N Gram Translation
... We are only aware of one work that performs a systematic comparison of smoothing techniques in phrase-based machine translation systems (Foster et al., 2006). Two types of phrase-table smoothing were ... See full document
9
Efficient Multi Pass Decoding for Synchronous Context Free Grammars
... chine translation decoding when using syn- chronous context-free grammars as the trans- lation model and n-gram language models: the first pass uses a bigram language model, and the resulting ... See full document
9
Cross Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single Corpus Evaluation Enough?
... Machine translation approaches are being presently ap- plied for GEC (Junczys-Dowmunt et ...a translation problem from the erroneous text to the correct text (Mizumoto et ... See full document
6
An Empirical Evaluation of Stop Word Removal in Statistical Machine Translation
... machine translation system by relaxing the complexity of the translation task by remov- ing the most frequent and predictable terms from the target language ...an n-gram based ... See full document
8
Less is More: Significance Based N gram Selection for Smaller, Better Language Models
... probabilities from the fully-smoothed model, which in the case of KN smoothing are designed specifically to cover N-grams that have not been observed, and are poor estimates for the probabilities of ... See full document
10
Syntax Based Word Ordering Incorporating a Large Scale Language Model
... sentence from a multi-set of input words (Wan et ...an N-gram language model, which has been used by text generation systems to improve ...an N-gram model by applying on- line ... See full document
11
N Gram Based Statistical Machine Translation versus Syntax Augmented Machine Translation: Comparison and System Combination
... The F astT ranslateChart beam-search de- coder was used as an engine of MER training aim- ing to tune the feature weight coefficients and pro- duce final n-best and 1-best translations by com- bining the intensive ... See full document
9
N gram based Machine Translation
... the translation system proposed here with other phrase-based translation systems is available through the results of the second shared task of the ACL 2005 workshop on “Building and using parallel ... See full document
24
Class Based n gram Models of Natural Language
... We estimate the parameters of an n-gram model by examining a sample of text, t~, which we call the training text, in a process called training.. To estimate the parameters of an n-gram m[r] ... See full document
14
Semantic Kernels for Semantic Parsing
... conditional models for sequence labeling such as Conditional Random Fields (CRFs) (Lafferty et ...basic CRF model was im- proved by means of reranking (Moschitti et ...obvious models for semantic ... See full document
7
LIMSI @ WMT’14 Medical Translation Task
... the translation is divided into two ...the translation step is monotonic, the peculiarity of this approach is to rely on the n-gram assumption to decompose the joint probability of a sentence ... See full document
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