Target-Side Context for Discriminative Models in Statistical Machine Translation

Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with source- and target-side topic distributions.With these topic distributions, we propose a topic dissimilarity model to select desirable (less dissimilar) rules by imposing penalties for rules with a large value of dissimilarity of their topic distributions to those of given documents. In order to encourage the use of non-topic specific translation rules, we also present a topic sensitivity model to balance translation rule selection between generic rules and topic-specific rules. Furthermore, we project target-side topic distributions onto the source-side topic model space so that we can benefit from topic information of both the source and target language. We integrate the proposed topic dissimilarity and sensitivity model into hierarchical phrase-based machine translation for synchronous translation rule selection. Experiments show that our topic-based translation rule selection model can substantially improve translation quality.

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Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding

Prague Bulletin of Mathematical Linguistics ◽

10.2478/pralin-2014-0002 ◽

2014 ◽

Vol 101 (1) ◽

pp. 29-41

Author(s):

Aleš Tamchyna ◽

Fabienne Braune ◽

Alexander Fraser ◽

Marine Carpuat ◽

Hal Daumé iii ◽

...

Keyword(s):

Open Source ◽

Machine Translation ◽

State Of The Art ◽

Statistical Machine Translation ◽

Sentence Context ◽

Discriminative Models ◽

Current State ◽

Source Sentence ◽

Independence Assumptions

Abstract Current state-of-the-art statistical machine translation (SMT) relies on simple feature functions which make independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule, which is available at decoding time. We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.

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Pre-Reordering for Neural Machine Translation: Helpful or Harmful?

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0018 ◽

2017 ◽

Vol 108 (1) ◽

pp. 171-182 ◽

Cited By ~ 5

Author(s):

Jinhua Du ◽

Andy Way

Keyword(s):

Machine Translation ◽

Statistical Machine Translation ◽

Word Class ◽

Word Embeddings ◽

Neural Machine Translation ◽

Parts Of Speech ◽

Translation Quality ◽

The Impact ◽

Japanese English ◽

Target Side

AbstractPre-reordering, a preprocessing to make the source-side word orders close to those of the target side, has been proven very helpful for statistical machine translation (SMT) in improving translation quality. However, is it the case in neural machine translation (NMT)? In this paper, we firstly investigate the impact of pre-reordered source-side data on NMT, and then propose to incorporate features for the pre-reordering model in SMT as input factors into NMT (factored NMT). The features, namely parts-of-speech (POS), word class and reordered index, are encoded as feature vectors and concatenated to the word embeddings to provide extra knowledge for NMT. Pre-reordering experiments conducted on Japanese↔English and Chinese↔English show that pre-reordering the source-side data for NMT is redundant and NMT models trained on pre-reordered data deteriorate translation performance. However, factored NMT using SMT-based pre-reordering features on Japanese→English and Chinese→English is beneficial and can further improve by 4.48 and 5.89 relative BLEU points, respectively, compared to the baseline NMT system.

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Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil

Digital ◽

10.3390/digital1020007 ◽

2021 ◽

Vol 1 (2) ◽

pp. 86-102

Author(s):

Akshai Ramesh ◽

Venkatesh Balavadhani Parthasarathy ◽

Rejwanul Haque ◽

Andy Way

Keyword(s):

Machine Translation ◽

State Of The Art ◽

Statistical Machine Translation ◽

Training Data ◽

Research Translation ◽

Neural Machine Translation ◽

Low Resource ◽

Evaluation Scheme ◽

Dominant Paradigm ◽

Target Side

Phrase-based statistical machine translation (PB-SMT) has been the dominant paradigm in machine translation (MT) research for more than two decades. Deep neural MT models have been producing state-of-the-art performance across many translation tasks for four to five years. To put it another way, neural MT (NMT) took the place of PB-SMT a few years back and currently represents the state-of-the-art in MT research. Translation to or from under-resourced languages has been historically seen as a challenging task. Despite producing state-of-the-art results in many translation tasks, NMT still poses many problems such as performing poorly for many low-resource language pairs mainly because of its learning task’s data-demanding nature. MT researchers have been trying to address this problem via various techniques, e.g., exploiting source- and/or target-side monolingual data for training, augmenting bilingual training data, and transfer learning. Despite some success, none of the present-day benchmarks have entirely overcome the problem of translation in low-resource scenarios for many languages. In this work, we investigate the performance of PB-SMT and NMT on two rarely tested under-resourced language pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain into consideration. This paper demonstrates our findings and presents results showing the rankings of our MT systems produced via a social media-based human evaluation scheme.

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Optimization for Statistical Machine Translation: A Survey

Computational Linguistics ◽

10.1162/coli_a_00241 ◽

2016 ◽

Vol 42 (1) ◽

pp. 1-54 ◽

Cited By ~ 7

Author(s):

Graham Neubig ◽

Taro Watanabe

Keyword(s):

Machine Translation ◽

Large Scale ◽

Nonlinear Models ◽

Statistical Machine Translation ◽

Risk Minimization ◽

Discriminative Models ◽

Scale Optimization ◽

State Of Affairs ◽

Translation Accuracy ◽

Minimum Error Rate Training

In statistical machine translation (SMT), the optimization of the system parameters to maximize translation accuracy is now a fundamental part of virtually all modern systems. In this article, we survey 12 years of research on optimization for SMT, from the seminal work on discriminative models (Och and Ney 2002) and minimum error rate training (Och 2003), to the most recent advances. Starting with a brief introduction to the fundamentals of SMT systems, we follow by covering a wide variety of optimization algorithms for use in both batch and online optimization. Specifically, we discuss losses based on direct error minimization, maximum likelihood, maximum margin, risk minimization, ranking, and more, along with the appropriate methods for minimizing these losses. We also cover recent topics, including large-scale optimization, nonlinear models, domain-dependent optimization, and the effect of MT evaluation measures or search on optimization. Finally, we discuss the current state of affairs in MT optimization, and point out some unresolved problems that will likely be the target of further research in optimization for MT.

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Factored Statistical Machine Translation for German-English

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.47 ◽

2018 ◽

Vol 5 (1) ◽

pp. 37-45

Author(s):

Darryl Yunus Sulistyan

Keyword(s):

Machine Translation ◽

English Language ◽

Statistical Machine Translation ◽

New Model ◽

Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

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