Performance Of Machine Translation System Research Articles

Comparison Between Human Translation and Machine Translation Take Lolita as an Example

Taking the excerpt of Lolita as the research corpus, this paper compares the differences between the manual translation and the machine translation, and finds that the machine translation system has some problems such as inaccuracy in semantic understanding and grammatical errors. In order to improve the quality of machine translation and promote cross-cultural communication and information flow,a classification system of translation errors is established and the errors in machine translation are summarized in detail. The proposed classification system will facilitate a comprehensive understanding of the limitations inherent in machine translation, enable an in-depth analysis of existing challenges, and serve as a valuable reference for enhancing the performance of machine translation systems, thereby elevating the quality and precision of automated translations.

A Review of Machine Translation Quality Assessment Methods

Machine translation quality assessment plays a crucial role in enhancing the performance of machine translation systems. This review aims to survey and outline the current major methods used for assessing machine translation quality, which can be broadly categorized into manual and automatic assessment methods. Upon analyzing the existing literature, it becomes evident that while manual evaluation methods yield high-quality results, they are time-consuming and labor-intensive. On the other hand, automatic evaluation methods are cost-effective and rapid, but their evaluation results do not match the expertise of human evaluators. The objective of this paper is to furnish researchers with a comprehensive overview of machine translation quality assessment methods, enabling them to select appropriate approaches based on their specific experimental requirements. Additionally, we aspire to offer valuable insights and novel perspectives for further advancements in the field of machine translation quality assessment methods.

Obtaining Parallel Sentences in Low-Resource Language Pairs with Minimal Supervision.

Machine translation relies on parallel sentences, the number of which is an important factor affecting the performance of machine translation systems, especially in low-resource languages. Recent advances in learning cross-lingual word representations from nonparallel data by machine learning make a new possibility for obtaining bilingual sentences with minimal supervision in low-resource languages. In this paper, we introduce a novel methodology to obtain parallel sentences via only a small-size bilingual seed lexicon about hundreds of entries. We first obtain bilingual semantic by establishing cross-lingual mapping in monolingual languages via a seed lexicon. Then, we construct a deep learning classifier to extract bilingual parallel sentences. We demonstrate the effectiveness of our methodology by harvesting Uyghur-Chinese parallel sentences and constructing a machine translation system. The experiments indicate that our method can obtain large and high-accuracy bilingual parallel sentences in low-resource language pairs.

Human Evaluation of English–Irish Transformer-Based NMT

In this study, a human evaluation is carried out on how hyperparameter settings impact the quality of Transformer-based Neural Machine Translation (NMT) for the low-resourced English–Irish pair. SentencePiece models using both Byte Pair Encoding (BPE) and unigram approaches were appraised. Variations in model architectures included modifying the number of layers, evaluating the optimal number of heads for attention and testing various regularisation techniques. The greatest performance improvement was recorded for a Transformer-optimized model with a 16k BPE subword model. Compared with a baseline Recurrent Neural Network (RNN) model, a Transformer-optimized model demonstrated a BLEU score improvement of 7.8 points. When benchmarked against Google Translate, our translation engines demonstrated significant improvements. Furthermore, a quantitative fine-grained manual evaluation was conducted which compared the performance of machine translation systems. Using the Multidimensional Quality Metrics (MQM) error taxonomy, a human evaluation of the error types generated by an RNN-based system and a Transformer-based system was explored. Our findings show the best-performing Transformer system significantly reduces both accuracy and fluency errors when compared with an RNN-based model.

Automatic Extraction of English-Chinese Translation Templates Based on Deep Learning

Translation templates are an important cause of knowledge in machine translation (MT) systems. Their quality and scale directly influence the performance of MT systems. How to obtain high-quality and efficient translation templates from corpora has become a hot topic in recent study. In this paper, a tree to String alignment template (TAT) based on syntactic structure is proposed. This template describes the alignment between the source language syntax tree and the target language string. The syntactic structure, a large number of construction tags, and variables are introduced into the template, which enables the syntactic model to deal with discontinuous phrases and has the ability of generalization. Templates can be used in syntactic statistics, case-based, and rule-based MT systems according to different decoders. ATTEBSC algorithm is a basic method to learn translation templates by comparing sentence pairs. It demands that sentence pairs be constructed in a precise comparison structure ahead of time, but there are no strict guidelines on how to do it. In this paper, we propose a method to calculate the specific comparison scheme using the longest common subsequence (LCS) and use the normalized LCS distance to screen sentences with high similarity and then use the ATTEBSC algorithm to automatically remove the template. Experiments show that this method is easy and effective, and many expensive templates can be learned.

Machine translation systems and quality assessment: a systematic review

Nowadays, in the globalised context in which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible to satisfy the demand for translation by relying only in human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due to their potential to overcome this problem. Consequently, research in this field is constantly growing and new MT paradigms are emerging. In this paper, a systematic literature review has been carried out in order to identify what MT systems are currently most employed, their architecture, the quality assessment procedures applied to determine how they work, and which of these systems offer the best results. The study is focused on the specialised literature produced by translation experts, linguists, and specialists in related fields that include the English–Spanish language combination. Research findings show that neural MT is the predominant paradigm in the current MT scenario, being Google Translator the most used system. Moreover, most of the analysed works used one type of evaluation—either automatic or human—to assess machine translation and only 22% of the works combined these two types of evaluation. However, more than a half of the works included error classification and analysis, an essential aspect for identifying flaws and improving the performance of MT systems.

A novel and robust approach for pro-drop language translation

A significant challenge for machine translation (MT) is the phenomena of dropped pronouns (DPs), where certain classes of pronouns are frequently dropped in the source language but should be retained in the target language. In response to this common problem, we propose a semi-supervised approach with a universal framework to recall missing pronouns in translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation has two phases: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our statistical MT (SMT) system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. To validate the robustness of our approach, we investigate our approach on both Chinese–English and Japanese–English corpora extracted from movie subtitles. Compared with an SMT baseline system, experimental results show that our approach achieves a significant improvement of +1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy for Chinese–English, and nearly +1 BLEU point with 58% F-score for Japanese–English. We believe that this work could help both MT researchers and industries to boost the performance of MT systems between pro-drop and non-pro-drop languages.

Automated MT evaluation metrics and their limitations

This paper gives a general overview of the main classes of methods for automatic evaluation of Machine Translation (MT) quality, their limitations and their value for professional translators and MT developers. Automated evaluation of MT characterizes performance of MT systems on specific text or a corpus. Automated scores are expected to correlate with certain parameters of MT quality scored by human evaluators, such as adequacy of fluency of translation. Automated evaluation is now part of MT development cycle, but it also contributes to fundamental research on MT and improving MT technology..

A Structure-Based Model for Chinese Organization Name Translation

Named entity (NE) translation is a fundamental task in multilingual natural language processing. The performance of a machine translation system depends heavily on precise translation of the inclusive NEs. Furthermore, organization name (ON) is the most complex NE for translation among all the NEs. In this article, the structure formulation of ONs is investigated and a hierarchical structure-based ON translation model for Chinese-to-English translation system is presented. First, the model performs ON chunking; then both the translation of words within chunks and the process of chunk-reordering are achieved by synchronous context-free grammar (CFG). The CFG rules are extracted from bilingual ON pairs in a training program. The main contributions of this article are: (1) defining appropriate chunk-units for analyzing the internal structure of Chinese ONs; (2) making the chunk-based ON translation feasible and flexible via a hierarchical CFG derivation; and (3) proposing a training architecture to automatically learn the synchronous CFG for constructing ONs with chunk-units from aligned bilingual ON pairs. The experiments show that the proposed approach translates the Chinese ONs into English with an accuracy of 93.75% and significantly improves the performance of a baseline statistical machine translation (SMT) system.

Segmentation and alignment of parallel text for statistical machine translation

We address the problem of extracting bilingual chunk pairs from parallel text to create training sets for statistical machine translation. We formulate the problem in terms of a stochastic generative process over text translation pairs, and derive two different alignment procedures based on the underlying alignment model. The first procedure is a now-standard dynamic programming alignment model which we use to generate an initial coarse alignment of the parallel text. The second procedure is a divisive clustering parallel text alignment procedure which we use to refine the first-pass alignments. This latter procedure is novel in that it permits the segmentation of the parallel text into sub-sentence units which are allowed to be reordered to improve the chunk alignment. The quality of chunk pairs are measured by the performance of machine translation systems trained from them. We show practical benefits of divisive clustering as well as how system performance can be improved by exploiting portions of the parallel text that otherwise would have to be discarded. We also show that chunk alignment as a first step in word alignment can significantly reduce word alignment error rate.

Recognition and Utilization of Clausal Relations in Complex Sentences for Improving the Performance of Machine Translation Systems

Machine translation systems are inefficient when translating complex sentences. An important reason for this, among others, comes from the fact that the systems translate a complex sentence without taking into account the clausal relation between main and subordinate clauses. We devise a method for recognizing and utilizing the clausal relation to make the machine translation systems self-improve their performance. First, we attempt to find the clausal relation between the two clauses in a complex sentence through a machine learning mechanism. Second, utilizing this information, we try to pre-edit or modify the sentence so that the clausal relation is expressed overtly in the modified sentence. Experiments with a machine translation system have shown that our idea and methodology improve the performance of the system.

Corpora in Translation Studies

Abstract Corpus-based research has become widely accepted as a factor in improving the performance of machine translation systems, and corpus-based terminology compilation is now the norm rather than the exception. Within translation studies proper, Lindquist (1984) has advocated the use of corpora for training translators, and Baker (1993a) has argued that theoretical research into the nature of translation will receive a powerful impetus from corpus-based studies. It is becoming increasingly important to take stock of what is happening on this front and to start working towards the development of an explicit and coherent methodology for corpus-based research in the discipline. This paper discusses the current and potential use of corpora in translation studies, with particular reference to theoretical issues.