Briefly Noted

This paper presents example-based machine translation (MT) based on syntactic transfer, which selects the best translation by using models of statistical machine translation. Example-based MT sometimes generates invalid translations because it selects similar examples to the input sentence based only on source language similarity. The method proposed in this paper selects the best translation by using a language model and a translation model in the same manner as statistical MT, and it can improve MT quality over that of 'pure' example-based MT. A feature of this method is that the statistical models are applied after word re-ordering is achieved by syntactic transfer. This implies that MT quality is maintained even when we only apply a lexicon model as the translation model. In addition, translation speed is improved by bottom-up generation, which utilizes the tree structure that is output from the syntactic transfer.

Large amounts of information is embedded in the natural scenes. Signs are good examples of objects in natural environments which have rich information content. In this paper, we present our efforts in the automatic sign translation. We describe the challenges in the automatic sign translation and introduce the architecture of our current system for automatic detection and translation of Chinese signs. Two data-driven machine translation methods: Example Based Machine Translation (EBMT) and Statistical Machine Translation (SMT) are compared for the task of translating Chinese signs into English. We report the experimental results of both methods that are trained from a small bilingual sign corpus combined with a bilingual glossary. The experiment results indicate that EBMT generates more correct translations while SMT is better at inferring unseen patterns. We are currently working on developing a multi-engine machine translation system that can incrementally learn from the data and combine the results from EBMT and SMT.

Statistical Machine Translation (SMT) has been the dominant corpus-based machine translation (MT) approach in the last twenty years. While SMT has been studied in detail among European languages, it has not been studied sufficiently in language pairs including Turkish as source or target language, and its study has been limited mostly to English ↔ Turkish language pair. This study aims to broaden the perspective on Turkish corpus-based MT studies by training MT engines between Turkish and a wide variety of languages with different features. It surveys customized SMT between Turkish and 11 different languages. Twenty-two SMT engines have been trained in KantanMT with open parallel corpora using Turkish as both source and target language. Three automatic evaluation metrics F-Measure, BLEU, and TER have been used for evaluating MT quality. Due to the variations in the corpus quality and size, highly varying results have been achieved. While Turkish ↔ Catalan engines have had the highest automatic evaluation scores, Turkish ↔ Arabic engines have had the lowest automatic scores. While the quality results are highly varying across languages, we obtain baseline scores for a wide variety of languages coupled with Turkish. These results may provide a reference point for evaluating future MT systems including Turkish.

Corpus-based machine translation (MT) has been the main approach to developing and implementing MT systems in both academia and the industry over the last three decades. In this field, the type and size of the corpus used for training MT engines have presented problems for both statistical MT (SMT) systems as well as neural MT (NMT) systems, being the two dominant corpusbased approaches. Moreover, language pairs such as Turkish-English have been understudied within this framework. This article aims to evaluate the translation quality in Turkish-to-English custom MT systems that have been trained on different corpus sizes and types. Two NMT engines and two SMT engines were trained on the KantanMT platform using two different training corpus types with either only domain-specific cardiology corpus or this corpus plus a mixed-domain corpus. The study conducted both automatic evaluations with metrics including BLEU, F-Measure and TER, as well as a comprehensive human evaluation with metrics including fluency, A/B test, and adequacy. Lastly, the study realized a separate, subjective terminology evaluation in order to investigate how differently MT systems handle terminology, as this is a crucial aspect for specific-domain text types such as cardiology. While the automatic evaluation results suggest the SMT engines to perform better than NMT engines, all human evaluators rated the mixed-domain NMT engine as the highest performing one. However, the terminology evaluation task demonstrated SMT to still be able to perform better and to commit less terminology errors, despite the industry and academia shifting toward NMT engines.

In this paper, we present our work on a case study between statistical machine translation (SMT) and rule-based machine translation (RBMT) systems on English-Indian language and Indian to Indian language perspective. Main objective of our study is to make a five-way performance comparison: such as, (a) SMT and RBMT; (b) SMT on English–Indian language; (c) RBMT on English–Indian language; (d) SMT on Indian to Indian language perspective; (e) RBMT on Indian to Indian language perspective. Through a detailed analysis, we describe the rule-based and the statistical machine translation system developments and its evaluations. Further, with a detailed error analysis, we point out the relative strengths and weaknesses of both the systems. The observations based on our study are: (a) SMT systems outperform RBMT; (b) In the case of SMT: English to Indian language MT systems perform better than Indian to English language MT systems; (c) In the case of RBMT: English to Indian language MT systems perform better than Indian to English language MT systems; (d) SMT systems perform better for Indian to Indian language MT systems compared to RBMT. Effectively, we shall see that even with a small amount of training corpus SMT system has many advantages for high-quality domain-specific machine translation over that of a rule-based counterpart.

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.

In terms of language technology, Maithili is a resource-poor language. It is spoken in India and Nepal and is one of the 22 scheduled languages in India. Maithili has almost no language technology resource. English in India happens to be a dominant language in terms of content and usage. However, since more than 90% of Indians do not use English, a translation from English to Maithili becomes critical. An absence of basic tools in this language has affected resource creation of machine translation (MT). The present work discusses efforts for language technology resource (LTR) creation and divergence study for a statistical English-Maithili MT (EMMT) system. Creating any statistical MT (SMT) system requires sizeable parallel, aligned corpora for training and testing. Creating general-purpose source corpora for English language and creating translation equivalents with possible alignments require time and effort. The paper focuses on the data collection methods, cleaning, the size and structure of the text corpora, alignment and parallelization strategies, training, testing, and a study of divergence between the language pair.

In the modern era, the field of Machine Translation (MT) has seen a significant shift towards Neural Machine Translation (NMT) techniques, which have surpassed traditional Statistical Machine Translation (SMT) models in terms of the quality of translation. Despite this, the efficacy of these techniques may differ based on the language combination in consideration. While SMT is somewhat more flexible in this regard, NMT often needs sizable parallel corpora to attain high translation accuracy. As a result, a benchmark system capable of offering sufficient translation for languages with limited resources, like Nepali, remains a pipe dream. This paper focuses on translating text using statistical and neural MT techniques for the under-resourced English-Nepali language pair. As a part of this system development, we built a parallel corpus of English-Nepali in the tourism domain. We explore the impact of different tokenization techniques on translation outcomes. A substantial analysis is also done for the performance of both approaches using automatic evaluation metrics, BLEU and TER. This paper aims to provide insights into the applicability of SMT and NMT for the under-resourced English-Nepali language pair in light of two popular epitomes of tokenization and to determine the most effective approach for achieving accurate translations.

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

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.

Neural versus phrase-based MT quality: An in-depth analysis on English–German and English–French

Quality Estimation (QE) and error analysis of Machine Translation (MT) output remain active areas in Natural Language Processing (NLP) research. Many recent efforts have focused on Machine Learning (ML) systems to estimate the MT quality, translation errors, post-editing speed or post-editing effort. As the accuracy of such ml tasks relies on the availability of corpora, there is an increasing need for large corpora of machine translations annotated with translation errors and the error annotation guidelines to produce consistent annotations. Drawing on previous work on translation error taxonomies, we present the SCATE (Smart Computer-aided Translation Environment) mt error taxonomy, which is hierarchical in nature and is based upon the familiar notions of accuracy and fluency. In the scate annotation framework, we annotate fluency errors in the target text and accuracy errors in both the source and target text, while linking the source and target annotations. We also propose a novel method for alignment-based Inter-Annotator Agreement (IAA) analysis and show that this method can be used effectively on large annotation sets. Using the scate taxonomy and guidelines, we create the first corpus of MT errors for the English-Dutch language pair, consisting of Statistical Machine Translation (SMT) and Rule-Based Machine Translation (RBMT) errors, which is a valuable resource not only for NLP tasks in this field but also to study the relationship between mt errors and post-editing efforts in the future. Finally, we analyse the error profiles of the smt and the rbmt systems used in this study and compare the quality of these two different mt architectures based on the error types.

Over the past ten years, neural machine translation (NMT) has seen tremendous growth and is now entering a phase of maturity. Despite being the most popular solution for machine translation (MT), it performs sub-optimally on under-resourced language pairs due to lack of parallel corpora as compared to high-resourced language pairs. The implementation of NMT techniques for under-resourced language pairs is receiving the attention of researchers and has resulted in a significant amount of research for many under-resourced language pairs. In view of the growth of MT, this paper describes a set of practical approaches for investigating MT between Kannada and Tulu. These two languages belong to the family of Dravidian languages and are under-resourced due to lack of tools and resources particularly the parallel corpus for MT. Since there are no parallel corpora for the Kannada-Tulu language pair for MT, this work aims to construct a parallel corpus for this language pair. As manual construction of parallel corpus is laborious, data augmentation is introduced to enhance the size of the parallel corpus along with suitable preprocessing techniques. Different NMT schemes such as recurrent neural network (RNN) baseline, bidirectional recurrent neural network (BiRNN), transformer-based NMT with and without subword tokenization, and statistical machine translation (SMT) models are implemented for MT of Kannada-Tulu and Tulu-Kannada language pairs. Empirical results reveal that the impact of data augmentation increases the bilingual evaluation understudy (BLEU) score of the proposed models. Transformer-based models with subword tokenization outperformed the other models with BLEU scores 41.82 and 40.91 for Kannada-Tulu and Tulu-Kannada MT, respectively.

Sanskrit is one of the earliest native languages and is correctly described as "the gods' language" because of its wide use in Indian religious literature from the past. However, it is becoming less popular in modern India. Due in significant part to the need for more materials for translation both in and out of Sanskrit, it is no longer commonly utilized. This study explores the feasibility of using machine translation (MT) to provide a link between Sanskrit and, one of the earliest native languages, and its contemporary descendant Hindi. A study was conducted between existing modelling methodologies, notably Statistical machine translation (SMT), and the proposed novel deep learning-based Machine translation strategy using a manually created parallel corpus for the Sanskrit-Hindi language pair. While SMT creates interpretations by mapping phrases from the languages of the source and destination, statistical models, and bilingual text corpora for learning parameters, neural machine translation (NMT) frequently models entire phrases in a single integrated model, using a convolutional neural network to calculate the probability of a word sequence. The proposed NMT model is implemented using an encoder-decoder with an attention mechanism paradigm and the inclusion of gated recurrent units. Our approach involved development of a novel model for Sanskrit-Hindi machine translation using deep learning and the creation of parallel corpora for the Sanskrit-Hindi language pair. The proposed model is evaluated on automated and human-based metrics, and results show that our proposed deep learning-based model outperforms statistical modelling techniques on Moses, surpassing them both with a BLEU score of 53.8% compared to 34.56%. This article examines the undiscovered area of machine translation from Sanskrit to Hindi and discusses the main benefits and drawbacks of statistical and neural machine translation while providing a fresh viewpoint on the subject.

We explore the augmentation of statistical machine translation models with features of the context of each phrase to be translated. This work extends several existing threads of research in statistical MT, including the use of context in example-based machine translation (Carl and Way, 2003) and the incorporation of word sense disambiguation into a translation model (Chan et al., 2007). The context features we consider use surrounding words and part-of-speech tags, local syntactic structure, and other properties of the source language sentence to help predict each phrase's translation. Our approach requires very little computation beyond the standard phrase extraction algorithm and scales well to large data scenarios. We report significant improvements in automatic evaluation scores for Chinese-to-English and English-to-German translation, and also describe our entry in the WMT-08 shared task based on this approach.