Automated Simultaneous Interpretation: Hints of a Cognitive Framework for Machine Translation

Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it shoul d fulfill the specific requirement of this language . Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve a s rules for the ordering of sentence constituents. These rules draw their information from the syntact ic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by usin g transfer-based approach. A system called Npae- Rbmt has been developed in this research. Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation. The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the mean ing of the original sentence in order to generate the correct translation. Using advantages of transf er-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic an alysis goes deeper-as the representation of analysi s step becomes more abstract. In fact, a major goal o f MT research is to define a level of analysis whic h is so deep in which transfer-based machine translat ion is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this a pproach for distant languages. Conclusion: Based on the achieved results, we have managed to perform th e syntactic reordering within an Arabic noun phrases to English translation task by using transf er-based machine translation and also achieved reasonable improvements in translation quality over related approach.

In this study, we explore the implications of dataset limitations in semantic knowledge-driven machine translation (MT) for intelligent virtual assistants (IVA). Our approach diverges from traditional single-best translation techniques, utilizing a multi-variant MT method that generates multiple valid translations per input sentence through a constrained beam search. This method extends beyond the typical constraints of specific verb ontologies, embedding within a broader semantic knowledge framework. We evaluate the performance of multi-variant MT models in translating training sets for Natural Language Understanding (NLU) models. These models are applied to semantically diverse datasets, including a detailed evaluation using the standard MultiATIS++ dataset. The results from this evaluation indicate that while multi-variant MT method is promising, its impact on improving intent classification (IC) accuracy is limited when applied to conventional datasets such as MultiATIS++. However, our findings underscore that the effectiveness of multi-variant translation is closely associated with the diversity and suitability of the datasets utilized. Finally, we provide an in-depth analysis focused on generating variant-aware NLU datasets. This analysis aims to offer guidance on enhancing NLU models through semantically rich and variant-sensitive datasets, maximizing the advantages of multi-variant MT.

We conduct the first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators. Our case study is Warbreaker, a popular fantasy novel originally written in English, which we translate into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences) with two data-driven approaches to Machine Translation (MT): phrase-based statistical MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained on over 100 million words of fiction. In the post-editing experiment, six professional translators with previous experience in literary translation translate subsets of this chapter under three alternating conditions: from scratch (the norm in the novel translation industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the time taken to translate each sentence, as well as the number of pauses and their duration. Based on these measurements, and using mixed-effects models, we study post-editing effort across its three commonly studied dimensions: temporal, technical and cognitive. We observe that both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort, post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but longer pauses (14 and 25%).

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

Background: Questionnaires are forward-translated and then back-translated by human translators during adaptation of a survey questionnaire in the local language. The machine translation method is now available free from “Google Translate” website and it can be used for translation from English to Bengali and vice versa. Using machine translation method may save researchers from investing money and time in translation by human experts. Aim: The aim of this pilot study was to test the feasibility of “Google Translate” in forward-translation and back-translation during adaptation of a survey questionnaire from English to Bengali. Materials and Methods: A survey questionnaire, originally developed in English, was forward-translated to Bengali and then back-translated to English by machine translation method and manual translation method. Both the versions were checked for errors expressed in subjective sentence error rate (SSER). Back-translated English questionnaire obtained from machine translation and manual translation was checked for equivalence with the original questionnaire. Results: SSER for forward-translated Bengali questionnaire by machine translation method was 66.60 ± 4.04 and manual translation method was 117.20 ± 1.30 (P

This article addresses the development of statistical models for phrase-based machine translation (MT) which extend a popular word-alignment model proposed by IBM in the early 90s. A novel decoding algorithm is directly derived from the optimization criterion which defines the statistical MT approach. Efficiency in decoding is achieved by applying dynamic programming, pruning strategies, and word reordering constraints. It is known that translation performance can be boosted by exploiting phrase (or multiword) translation pairs automatically extracted from a parallel corpus. New phrase-based models are obtained by introducing extra multiwords in the target language vocabulary and by estimating the corresponding parameters from either: (i) a word-based model, (ii) phrase-based statistics computed on the parallel corpus, or (iii) the interpolation of the two previous estimates. Word-based and phrase-based MT models are evaluated on a traveling domain task in two translation directions: Chinese-English (12k-word vocabulary) and Italian-English (16k-word vocabulary). Phrase-based models show Bleu score improvements over the word-based model by 19% and 13% relative, respectively.

This thesis presents different contributions in the fields of fully-automatic statistical machine translation and interactive statistical machine translation. In the field of statistical machine translation there are three problems that are to be addressed, namely, the modelling problem, the training problem and the search problem. In this thesis we present contributions regarding these three problems. Regarding the modelling problem, an alternative derivation of phrase-based statistical translation models is proposed. Such derivation introduces a set of statistical submodels governing different aspects of the translation process. In addition to this, the resulting submodels can be introduced as components of a log-linear model. Regarding the training problem, an alternative estimation technique for phrase-based models that tries to reduce the strong heuristic component of the standard estimation technique is proposed. The proposed estimation technique considers the phrase pairs that compose the phrase model as part of complete bisegmentations of the source and target sentences. We theoretically and empirically demonstrate that the proposed estimation technique can be efficiently executed. Experimental results obtained with the open-source Thot toolkit also presented in this thesis, show that the alternative estimation technique obtains phrase models with lower perplexity than those obtained by means of the standard estimation technique. However, the reduction in the perplexity of the model did not allow us to obtain improvements in the translation quality. To deal with the search problem, we propose a search algorithm which is based on the branch-and-bound search paradigm. The proposed algorithm generalises different search strategies that can be accessed by modifying the input parameters. We carried out experiments to evaluate the performance of the proposed search algorithm. Additionally, we also study an alternative formalisation of the search problem in which the best alignment at phrase-level is obtained given the source and target sentences. To solve this problem, smoothing techniques are applied over the phrase table. In addition to this, the standard search algorithm for phrase-based statistical machine translation is modified to explore the space of possible alignments. Empirical results show that the proposed techniques can be used to efficiently and robustly generate phrase-based alignments. One disadvantage of phrase-based models is its huge size when they are estimated from very large corpora. In this thesis, we propose techniques to alleviate this problem during both the estimation and the decoding stages. For this purpose, main memory requirements are transformed into hard disk requirements. Experimental results show that the hard disk accesses do not significantly decrease the efficiency of the SMT system. With respect to the contributions in the field of interactive statistical machine translation, on the one hand, we present alternative techniques to implement interactive machine translation systems. On the other hand, we give a proposal of an interactive machine translation system which is able to learn from user-feedback by means of online learning techniques. We propose two alternative techniques for interactive statistical machine translation. The first one is based on the generation of partial alignments at phrase level. This approach constitutes an application of the phrase-based alignment generation techniques that are also proposed in this thesis. The second proposal tackles the interactive machine translation process by means of word graphs and stochastic error-correction models. The proposed approach differs from other existing approaches described in the literature in the introduction of error-correction techniques in the statistical framework of the interactive machine translation process. We carried out experiments to evaluate the two proposed techniques, showing that they are competitive with state-of-the-art interactive machine translation systems. In addition to this, such techniques have been used to implement an interactive machine translation prototype following a client-server architecture. Finally, the above mentioned interactive machine translation system with online learning is based on the use of statistical models that can be incrementally updated. The main difficulty defining incremental versions of the statistical models involved in the interactive translation process appears when such models are estimated by means of the expectation-maximisation algorithm. To solve this problem, we propose the application of the incremental version of such algorithm. The proposed interactive machine translation system with online learning was empirically evaluated, demonstrating that the system is able to learn from scratch or from previously estimated models. In addition to this, the obtained results also show that the interactive machine translation system with online learning significantly outperforms other state-of-the-art systems described in the literature.

Machine translation (MT) technology has developed for many decades and can provide high quality reference translation for certain fields. The main MT methods, on-line MT systems and their defects are introduced by analyzing the necessity of combing modern information technology with translation teaching. The application of the online MT systems in translation teaching is discussed in the aspect of the objectives and patterns. To apply computer-aided techniques into translation teaching will promote the reform of translation teaching and meet the social requirements for the training of translators.

Machine translation is used to implement the translation between different languages. Neural machine translation is one of the most popular machine translation methods which have made a great progress in recent years especially on universal languages. However, domestic translation software for non-universal languages is limited and also needs improving. In this paper, we carry out a practice on neural machine translation from Indonesian to Chinese. We first build a bilingual corpus of Indonesian and Chinese. After that, we train translation models using neural machine translation method, with traditional statistical machine translation models as baselines. In the later stages of our practice, we develop a Web software, named as Lore Translator, basing on our so-far best translation model. The performance of our model is comparable to previous work.

In natural Language Processing, Machine Translation is still challenging for low resource regional languages like Hindi and Punjabi because of morphology, script variations and Semantic Ambiguity. Previous research on Hindi- Punjabi translation has mainly focused on distinct methodologies like dictionary-based machine translation, rule-based translation, statistical machine translation and pure neural machine translation. This research proposes a cascade Hybrid MT framework for Hindi-Punjabi Translation integrating Dictionary-based direct mapping for known words, Example based machine translation (EBMT) for similar sentence patterns and Neural Machine Translation for complex linguistic sentences. The proposed system optimizes computational efficiency through knowledge-driven pipelines that prioritize low-cost translation methods before invoking expensive neural networks, therefore proposed hybrid machine translation framework is significantly more computationally efficient than pure Neural Machine Translation (NMT) models. Experimental results demonstrate that the hybrid approach achieves superior performance compared to individual methods, with BLEU scores of 42.5 for dictionary matches, 38.2 for EBMT matches, and 35.7 for NMT translations.

This paper presents a practical machine translation system based on sentence types for economic news stories.Conventional English-to-Japanese machine translation (MT) systems which are rule-based approaches, are difficult to translate certain types of Associated Press (AP) wire service news stories, such as economics and sports, because these topics include many fixed expressions (such as compound words or collocations) which are difficult to be processed by conventional syntactic analysis and/or word selection methods.The proposed MT system, an economic-news stories machine translation system (ENTS), can translate economic news sentences with fixed expressions. The system consists of three processes, to handle different types of sentences, fixed type, economics-specific type and general type. This paper focuses mainly on the translation method for fixed-type sentences, which is a kind of example-based approach. In this translation method, fixed sentence translation (STRA) data plays a key role. The STRA data is a set of bilingual templates, which is built automatically from fixed English sentences and their Japanese translation equivalents. The fixed English sentences are extracted automatically from the AP corpus.A series of experiments to evaluate ENTS using economic news in the AP news stories showed the translation accuracy was about 50% higher than with our conventional rule-based MT method.

In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms.Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persian Statistical Machine Translation (SMT) paradigm.

Computer technology plays a major role in human's life, so the automatic translation perspectives are important for making communication possible among each other. Translation from one regional language to another national or international language is important to understand the information and technology expressed in that language. This led to the inception of machine translation from regional language to national or international language. Machine translation is a field of Natural Language Processing (NLP) and Artificial Intelligence (AI), where it deals with translation from one language to another language. The statistical machine translation approach is popular in automatic translation research area with good accuracy. This paper gives a review of the work done in Punjabi and Urdu languages. To the best of our knowledge, no work is available on Punjabi to Urdu MTS methodology, but Urdu to Punjabi MTS is already developed [Umrinder Pal Singh et. al, 2016]. Punjabi is a regional language as well as official language of Indian state, Punjab. All state government related work within Punjab is done in Punjabi language only, written in two scripts i.e. Gurmukhi and Shahmukhi. Urdu is an international language, where most of the Muslim states/countries use Urdu language only. This paper also surveys the various Indian Machine Translation systems and existing methods for evaluating the translated output of various MTS.

Assembling translations from multi-engine machine translation outputs

Applying convolutional attention mechanisms and Human Memory Search for effective English-Urdu translation