Analysis of Dataset Limitations in Semantic Knowledge-Driven Multi-Variant Machine Translation

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.

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.

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

Enhancing end-to-end speech translation via multi-stage knowledge distillation.

Machine translation is a computer-based translation process that receives a set of words from a particular humanreadable language as input and outputs a second set of words in the intended human-readable language. Machine translation has assisted linguists and sociologists all across the world. A machine translation model may be statistical, rule-based, or based on neural networks. The Neural Machine Translation (NMT) model was introduced in response to the numerous drawbacks of rule- based and statistical-based machine translation models. This study adds a parallel corpus of Assamese and English and develops the NMT system, a transformer model with a self- attention mechanism. For translation from English to Assamese, the system received a higher bilingual evaluation understudy (BLEU) score of 7.6, and for translation from Assamese to English,a BLEU score of 23.2.

This work aims to present an overview of using some artificial intelligence (AI) models in machine translation (MT). This work aims to integrate machine learning (ML), deep learning (DL), large language models (LLMs) to enhance machine translation between natural languages. The focus is directed to present the neural-based machine translation (NMT), and some DL models are presented. The bidirectional-encoder-representation from transformer (BERT) and LLMs are presented to utilize the big amount of textual data to learn translation patterns. The main measurable criteria that are used to evaluate the performance of MT and Arabic machine translation (AMT) are also presented. Some linguistic and technical challenges of MT and AMT are discussed. Some key points of future works in NMT are mentioned. A comparative study among some recent published related works is presented. A critical survey is presented to show the important role of DL and LLMs in MT. Some open-source toolkits, datasets and some commercial MT systems are collected and briefly presented. This work is expected to be useful for those people interested to know the up-to-date knowledge of MT and the potential role of DL and LLM in automatic translation.

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.

In order to solve the problems of time-consuming and frequent mistranslations in traditional translation methods, this study designed an English-Chinese machine translation method based on transfer learning. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning. On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The benefit of the proposed method includes a short translation time and fewer mistranslations.

LLMs have become a go-to solution not just for text generation, but also for natural language understanding (NLU) tasks.Acquiring extensive knowledge through language modeling on web-scale corpora, they excel on English NLU, yet struggle to extend their NLU capabilities to underrepresented languages.In contrast, machine translation models (MT) produce excellent multilingual representations, resulting in strong translation performance even for lowresource languages.MT encoders, however, lack the knowledge necessary for comprehensive NLU that LLMs obtain through language modeling training on immense corpora.In this work, we get the best both worlds by integrating MT encoders directly into LLM backbones via sample-efficient self-distillation.The resulting MT-LLMs preserve the inherent multilingual representational alignment from the MT encoder, allowing lower-resource languages to tap into the rich knowledge embedded in English-centric LLMs.Merging the MT encoder and LLM in a single model, we mitigate the propagation of translation errors and inference overhead of MT decoding inherent to discrete translation-based cross-lingual transfer (e.g., translate-test).Evaluation spanning three prominent NLU tasks and 127 predominantly low-resource languages renders MT-LLMs highly effective in cross-lingual transfer.MT-LLMs substantially and consistently outperform translate-test based on the same MT model, showing that we truly unlock multilingual language understanding for LLMs. Experimental Setup Tasks and LanguagesWe evaluate on two established classification tasks and one multiple-choice machine reading compre-

Stochastic finite-state models are efficiently learnable from data, effective for decoding and are associated with a calculus for composing models which allows for tight integration of constraints from various levels of language processing. In this paper, we present a method for stochastic finite-state machine translation that is trained automatically from pairs of source and target utterances. We use this method to develop models for English-Japanese and Japanese-English translation. We have embedded the Japanese-English translation system in a call routing task of unconstrained speech utterances. We evaluate the efficacy of the translation system in the context of this application.

This discussion paper presents and analyses the main conceptual differences and similarities between the human task of simultaneous interpretation and the statistical approach to machine translation. A psycho-cognitive model of the simultaneous interpretation process is reviewed and compared with the phrase-based statistical machine translation approach. Some interesting differences are identified and their possible implications on machine translation methods are discussed. Finally, the most relevant research problems related to them are identified.

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.

With the continuous advancement of artificial intelligence in natural language processing technology, machine translation based on machine learning technology has been fully transformed from traditional machine translation methods to neural network machine translation methods. In particular, the tremendous development of large families has made data-driven a reality. With deep learning as the research and design background, the neural network structure of random matrix multivariate statistical analysis is designed according to the language characteristics of English. The model was tested on a Chinese-English panning model, and the optimal model fused with a Bleu value of 39.53. The model results were applied to a real system to achieve language detection, multidirectional language translation, and manual correction of results to be able to learn long dependencies and overcome the limitations of recurrent neural networks to translate long sentences more fluently.

In a multilingual diversity country like India, language translation plays a significant factor in the area of text processing application such as information extraction, machine learning, natural language understanding, information retrieval and machine translation. Thereexists many challenges and issues in translation between languages, especially the Dravidian language, such as ambiguities, lexical divergence, syntactic, lexical mismatches and semantic issues, etc. The n — gram language model (LM) performs very well machine translation. However the existing approach is not efficient for generating n — gram from large bi-lingual parallel corpora. Most existing approaches are limited to monolingual to minimize redundant n — gram. To overcome this, this work presents an efficient machine translation model using machine learning techniques. No prior work has considered machine translation of Kannada and Telugu. The experimentis conducted on Wikipedia dataset show significant performance in term of accuracy and computation complexity of alignment considering different threshold.

While various neural machine translation (NMT) methods have integrated multiple prior knowledge to guide the translation, no research is available on combining with source-target bilingual translation template. In this paper, we firstly propose a maximal-length noun phrase template (MNP-Template), which constructs a novel translation template focusing on the constituency syntactic structure. Secondly, building on the multi-source transformer framework, we design a template-based machine translation (TBMT) model to integrate the syntactic knowledge of the retrieved target template similar to the ground-truth translation in the NMT decoder. Experiment results show the effectiveness of MNP- Template and TBMT on test subsets filtered by the fuzzy match score. Moreover, our method achieves significant improvement in out-of-domain test sets, which well-validated the university across diverse domains.