Statistical Machine Translation as a Language Model for Handwriting Recognition

2012 ◽  
Author(s):  
Jacob Devlin ◽  
Matin Kamali ◽  
Krishna Subramanian ◽  
Rohit Prasad ◽  
Prem Natarajan
Keyword(s):  
Machine Translation ◽  
Handwriting Recognition ◽  
Statistical Machine Translation ◽  
Language Model

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

scholarly journals Pushdown Automata in Statistical Machine Translation

2014 ◽  
Vol 40 (3) ◽  
pp. 687-723 ◽  
Author(s):  
Cyril Allauzen ◽  
Bill Byrne ◽  
Adrià de Gispert ◽  
Gonzalo Iglesias ◽  
Michael Riley
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
Complexity Analysis ◽  
Statistical Machine Translation ◽  
Language Model ◽  
General Purpose ◽  
Language Models ◽  
Experimental Conditions ◽  
Context Free ◽  
Pushdown Automata

This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent the space of candidate translations generated by the grammar when applied to an input sentence. General-purpose PDA algorithms for replacement, composition, shortest path, and expansion are presented. We describe HiPDT, a hierarchical phrase-based decoder using the PDA representation and these algorithms. We contrast the complexity of this decoder with a decoder based on a finite state automata representation, showing that PDAs provide a more suitable framework to achieve exact decoding for larger synchronous context-free grammars and smaller language models. We assess this experimentally on a large-scale Chinese-to-English alignment and translation task. In translation, we propose a two-pass decoding strategy involving a weaker language model in the first-pass to address the results of PDA complexity analysis. We study in depth the experimental conditions and tradeoffs in which HiPDT can achieve state-of-the-art performance for large-scale SMT.

scholarly journals Neural text normalization with adapted decoding and POS features

2019 ◽  
Vol 25 (5) ◽  
pp. 585-605
Author(s):  
T. Ruzsics ◽  
M. Lusetti ◽  
A. Göhring ◽  
T. Samardžić ◽  
E. Stark
Keyword(s):  
Machine Translation ◽  
Computer Mediated Communication ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Translation System ◽  
Swiss German ◽  
Part Of Speech ◽  
Word Level ◽  
Speech Transcription ◽  
Text Normalization

AbstractText normalization is the task of mapping noncanonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. This task is especially important for languages such as Swiss German, with strong regional variation and no written standard. In this paper, we propose a novel solution for normalizing Swiss German WhatsApp messages using the encoder–decoder neural machine translation (NMT) framework. We enhance the performance of a plain character-level NMT model with the integration of a word-level language model and linguistic features in the form of part-of-speech (POS) tags. The two components are intended to improve the performance by addressing two specific issues: the former is intended to improve the fluency of the predicted sequences, whereas the latter aims at resolving cases of word-level ambiguity. Our systematic comparison shows that our proposed solution results in an improvement over a plain NMT system and also over a comparable character-level statistical machine translation system, considered the state of the art in this task till recently. We perform a thorough analysis of the compared systems’ output, showing that our two components produce indeed the intended, complementary improvements.

scholarly journals Translation Mechanism of Neural Machine Algorithm for Online English Resources

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanping Ye
Keyword(s):  
Data Processing ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Processing Methods ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Sentence Similarity ◽  
Alignment Structure ◽  
Data Processing Methods

At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness. This paper proposes a framework that integrates vocabulary alignment structure for neural machine translation at the vocabulary level. Under the proposed framework, the neural machine translation decoder receives external vocabulary alignment information during each step of the decoding process to further alleviate the problem of missing vocabulary alignment structure. Specifically, this article uses the word alignment structure of statistical machine translation as the external vocabulary alignment information and introduces it into the decoding step of neural machine translation. The model is mainly based on neural machine translation, and the statistical machine translation vocabulary alignment structure is integrated on the basis of neural networks and continuous expression of words. In the model decoding stage, the statistical machine translation system provides appropriate vocabulary alignment information based on the decoding information of the neural machine translation and recommends vocabulary based on the vocabulary alignment information to guide the neural machine translation decoder to more accurately estimate its vocabulary in the target language. From the aspects of data processing methods and machine translation technology, experiments are carried out to compare the data processing methods based on language model and sentence similarity and the effectiveness of machine translation models based on fusion principles. Comparative experiment results show that the data processing method based on language model and sentence similarity effectively guarantees data quality and indirectly improves the algorithm performance of machine translation model; the translation effect of neural machine translation model integrated with statistical machine translation vocabulary alignment structure is compared with other models.

scholarly journals Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation

2010 ◽  
Vol 93 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Yvette Graham
Keyword(s):  
Open Source ◽  
Linear Combination ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Beam Search ◽  
Translation Model ◽  
Transfer Rules ◽  
Log Linear

Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation In this paper, we describe an open source transfer decoder for Deep Syntactic Transfer-Based Statistical Machine Translation. Transfer decoding involves the application of transfer rules to a SL structure. The N-best TL structures are found via a beam search of TL hypothesis structures which are ranked via a log-linear combination of feature scores, such as translation model and dependency-based language model.

scholarly journals Providing Morphological Information for SMT Using Neural Networks

2017 ◽  
Vol 108 (1) ◽  
pp. 271-282 ◽  
Author(s):  
Peyman Passban ◽  
Qun Liu ◽  
Andy Way
Keyword(s):  
Neural Networks ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Language Modeling ◽  
Word Embeddings ◽  
Surface Form ◽  
Complex Word ◽  
Complex Words

Abstract Treating morphologically complex words (MCWs) as atomic units in translation would not yield a desirable result. Such words are complicated constituents with meaningful subunits. A complex word in a morphologically rich language (MRL) could be associated with a number of words or even a full sentence in a simpler language, which means the surface form of complex words should be accompanied with auxiliary morphological information in order to provide a precise translation and a better alignment. In this paper we follow this idea and propose two different methods to convey such information for statistical machine translation (SMT) models. In the first model we enrich factored SMT engines by introducing a new morphological factor which relies on subword-aware word embeddings. In the second model we focus on the language-modeling component. We explore a subword-level neural language model (NLM) to capture sequence-, word- and subword-level dependencies. Our NLM is able to approximate better scores for conditional word probabilities, so the decoder generates more fluent translations. We studied two languages Farsi and German in our experiments and observed significant improvements for both of them.

Backward and trigger-based language models for statistical machine translation

2013 ◽  
Vol 21 (2) ◽  
pp. 201-226 ◽  
Author(s):  
DEYI XIONG ◽  
MIN ZHANG
Keyword(s):  
Mutual Information ◽  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Experimental Results ◽  
Language Models ◽  
Knowledge Sources ◽  
Long Distance ◽  
Translation Quality

AbstractThe language model is one of the most important knowledge sources for statistical machine translation. In this article, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We introduce algorithms to integrate the two proposed models into two kinds of state-of-the-art phrase-based decoders. Our experimental results on Chinese/Spanish/Vietnamese-to-English show that both models are able to significantly improve translation quality in terms of BLEU and METEOR over a competitive baseline.

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