Statistical Machine Translation Techniques Research Articles

Exploring Web-Based Translation Resources Applied to Hindi-English Cross-Lingual Information Retrieval

Internet users perceive a multilingual web but are unfamiliar with it due to communication in their regional language called Cross-Lingual Information Retrieval (CLIR). In CLIR, a translation technique is used to translate the user queries into the target document’s language. Conventional translation techniques are based on either a manual dictionary or a parallel corpus, whereas the trending Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) techniques are trained on a parallel corpus. NMT is not so mature for Hindi-English translation, according to the literature, and SMT performs better than the NMT. SMT provides a static translation due to the limited vocabularies in the available parallel corpus. It may not provide the translations for missing or unseen words, whereas the web provides a dynamic interface where multiple users are updating information at the same time. The web may provide the translations for missing or unseen words, and therefore the web is effectively used for technically developed languages like English, German, Spanish, Russian, and Chinese. In this article, different web resources such as Wikipedia, Hindi WordNet and Indo WordNet, ConceptNet, and online dictionary based translation techniques are proposed and applied to Hindi-English CLIR. Wikipedia-based translation approach incorporates three modules—exactly matched, partially matched, and disambiguation—to address the issues of wrong inter-wiki links, partially matched terms, and ambiguous articles. Hindi WordNet and Indo WorNet attribute “English synset” and ConceptNet attributes “Related term” & “Synonymy” are used for obtaining translations. Further, WordNet path similarity is used to disambiguate translations. Various online dictionaries are available that return multiple relevant and irrelevant translations. The proposed approaches are compared to the SMT where the Wikipedia-based approach achieves approximately similar mean average precision to SMT.

Intelligent Translation System Aiding High-Quality Writing in English in the Age of the Internet

Abstract This study focuses on applying intelligent translation systems in assisting high-quality English writing in the Internet era. The study analyzes statistical machine translation techniques, especially N-gram language modeling and word alignment techniques, and their crucial role in translation quality improvement. In English writing, the intelligent translation system significantly improves the quality of students’ Writing through word block translation. By analyzing 320 students, the mean value of self-efficacy in writing skills of students in the high-quality writing group was 3.78, significantly higher than that of the low-quality group, which was 2.82. After the experiment, 0.57 students indicated that they would improve their English writing vocabulary with the aid of the Intelligent Translation System, which showed the potential of the Intelligent Translation System to enhance students’ interest in and autonomy in Writing. Average distribution analysis shows that word block usage positively correlates with writing performance, with an R Square value of 0.6726. The intelligent translation system improves students’ English writing and enhances their self-efficacy, which is of great significance to English teaching.

A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu)

NMT (Neural Machine Translation) is an innovative approach in the field of machine translation, in contrast to SMT (statistical machine translation) and Rule-based techniques which has resulted annotable improvements. This is because NMT is able to overcome many of the shortcomings that are inherent in the traditional approaches. The Development of NMT has grown tremendously in the recent years but NMT performance remain under optimal when applied to low resource language pairs like Telugu, Tamil and Hindi. In this work a proposedmethod fortranslating pairs (Telugu to English) is attempted, an optimal approach which enhancesthe accuracy and execution time period.A hybrid method approach utilizing Long short-term memory (LSTM) and traditional Recurrent Neural Network (RNN) are used for testing and training of the dataset. In the event of long-range dependencies, LSTM will generate more accurate results than a standard RNN would endure and the hybrid technique enhances the performance of LSTM. LSTM is used during the encoding and RNN is used in decoding phases of NMT. Moth Flame Optimization (MFO) is utilized in the proposed system for the purpose of providing the encoder and decoder model with the best ideal points for training the data.

RNN-LSTM-GRU based language transformation

In past, rule-based and statistical machine translation techniques were employed to solve Urdu transliteration techniques. As mentioned in the literature, Urdu is considered as low-resource language. An impressive effort has been made for Arabic, French and Chinese language transliteration as compared to the Urdu language. Machine translation of Urdu language is a challenging problem. A very minute research work has been conducted toward Urdu transliteration. Factors behind the ignorance of Urdu language in research may be for its morphological complexity, diversity and most importantly due to the lack of reasonable bilingual parallel dataset. Getting a corpus for a language transliteration is the main resource to work on. This paper demonstrates the application of neural machine translation (NMT) for Urdu language transliteration, with the emphasis on contextual coverage of a language, which helps to improve transliteration accuracy. Build a robust NMT model which delivers efficient performance when trained over bilingual parallel corpora. Neural machine translation is an emerging technique depicting impressive performance, better than traditional MT methods in multiple aspects. In this research, we build the NMT model for the Urdu language to improve transliteration quality. An attention-based encoder–decoder system is proposed, and our experiment proves the efficiency of the proposed approach. To the best of our knowledge, this is the first effort for Urdu language bidirectional transliteration toward neural machine translation.

A new model for persian multi-part words edition based on statistical machine translation

Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some serious issues in Persian text processing and text readability. In order to cope with the issues, this work proposes a new model to correct spacing in multi-part words. The proposed method is based on statistical machine translation paradigm. In machine translation paradigm, text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The proposed method uses statistical machine translation techniques considering unedited multi-part words as a source language and the space-edited multi-part words as a destination language. The results show that the proposed method can edit and improve spacing correction process of Persian multi-part words with a statistically significant accuracy rate.

A Survey of Automated Text Simplification

Text simplification modifies syntax and lexicon to improve the understandability of language for an end user. This survey identifies and classifies simplification research within the period 1998-2013. Simplification can be used for many applications, including: Second language learners, preprocessing in pipelines and assistive technology. There are many approaches to the simplification task, including: lexical, syntactic, statistical machine translation and hybrid techniques. This survey also explores the current challenges which this field faces. Text simplification is a non-trivial task which is rapidly growing into its own field. This survey gives an overview of contemporary research whilst taking into account the history that has brought text simplification to its current state.

How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages

Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8% of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0% of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.

Statistical Machine Translation for Speech: A Perspective on Structures, Learning, and Decoding

In this paper, we survey and analyze state-of-the-art statistical machine translation (SMT) techniques for speech translation (ST). We review key learning problems, and investigate essential model structures in SMT, taking a unified perspective to reveal both connections and contrasts between automatic speech recognition (ASR) and SMT. We show that phrase-based SMT can be viewed as a sequence of finite-state transducer (FST) operations, similar in spirit to ASR. We further inspect the synchronous context-free grammar (SCFG)-based formalism that includes hierarchical phrase-based and many linguistically syntax-based models. Decoding for ASR, FST-based, and SCFG-based translation is also presented from a unified perspective as different realizations of the generic Viterbi algorithm on graphs or hypergraphs. These consolidated perspectives are helpful to catalyze tighter integrations for improved ST, and we discuss joint decoding and modeling toward coupling ASR and SMT.

An overview of the phrase-based statistical machine translation techniques

AbstractThis work provides a general overview of the statistical machine translation (SMT) scientific field, which is a subfield of machine translation (MT). Specifically, this paper focuses on one of the most popular SMT approaches, that is, the phrase-based system.The phrase-based translation units are typically extracted using statistical criteria, and they are weighted using different models. These models are log-linearly combined in the decoding, which is in charge of choosing the most probable translation. Significant quality improvements have been produced from original phrase-based SMT systems. Among others, the main challenges are reordering, domain adaptation and evaluation.

DOCUMENT TRANSLATION RETRIEVAL BASED ON STATISTICAL MACHINE TRANSLATION TECHNIQUES

We compare different strategies to apply statistical machine translation techniques in order to retrieve documents that are a plausible translation of a given source document. Finding the translated version of a document is a relevant task; for example, when building a corpus of parallel texts that can help to create and evaluate new machine translation systems. In contrast to the traditional settings in cross-language information retrieval tasks, in this case both the source and the target text are long and, thus, the procedure used to select which words or phrases will be included in the query has a key effect on the retrieval performance. In the statistical approach explored here, both the probability of the translation and the relevance of the terms are taken into account in order to build an effective query.

Rewriting the orthography of SMS messages

AbstractElectronic written texts used in computer-mediated interactions (emails, blogs, chats, and the like) contain significant deviations from the norm of the language. This paper presents the detail of a system aiming at normalizing the orthography of French SMS messages: after discussing the linguistic peculiarities of these messages and possible approaches to their automatic normalization, we present, compare, and evaluate various instanciations of a normalization device based on weighted finite-state transducers. These experiments show that using an intermediate phonemic representation and training, our system outperforms an alternative normalization system based on phrase-based statistical machine translation techniques.

Automatic Acquisition of Paraphrases Using Bilingual Dependency Relations

ABSTRACT ⎯ This letter introduces a new method to automatically acquire paraphrases using bilingual corpora. It utilizes the bilingual dependency relations obtained by projecting a monolingual dependency parse onto the other language’s sentence based on statistical alignment techniques. Since the proposed paraphrasing method can clearly disambiguate the sense of the original phrases using the bilingual context of dependency relations, it would be possible to obtain interchangeable paraphrases under a given context. Through experiments with parallel corpora of Korean and English language pairs, we demonstrate that our method effectively extracts paraphrases with high precision, achieving success rates of 94.3% and 84.6%, respectively, for Korean and English. Keywords ⎯ Paraphrase, bilingual dependency parsing, alignment, sense disambiguation, dependency relation. I. Introduction Approaches based on bilingual corpora are promising for the automatic acquisition of translation knowledge. Phrase-based statistical machine translation (SMT) models have advanced the state of the art in machine translation by expanding the basic unit from words to phrases [1], [2]. However, phrase-based SMT techniques suffer from data sparseness problems, such as unreliable translation probabilities of low-frequency phrases and low coverage, in that many phrases encountered at run-time are not observed in the training data. An alternative to these problems is to use paraphrases. In this study, we introduce a method of automatically acquiring paraphrases to smooth the translation parameters and to increase the coverage of translation knowledge. One previous approach

Statistical translation with scarce resources: A south african case study

Statistical machine translation techniques offer great promise for the development of automatic translation systems. However, the realization of this potential requires the availability of significant amounts of parallel bilingual texts. This paper reports on an attempt to reduce the amount of text that is required to obtain an acceptable translation system, through the use of active and semi- supervised learning. Systems were built using resources collected from South African government websites and the results evaluated using a standard automatic evaluation metric (BLEU). We show that significant improvements in translation quality can be achieved with very limited parallel corpora, and that both active learning and semi-supervised learning are useful in this context.