Relevant bibliographies by topics / Bidirectional Machine Translation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bidirectional Machine Translation'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Bidirectional Machine Translation"

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Elisaye, Bekele Milke. "Bidirectional English to Wolaytta Machine Translation Using a Hybrid Approach." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.B1028.15020525/.

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<strong>Abstract: </strong>As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two
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Zhou, Long, Jiajun Zhang, and Chengqing Zong. "Synchronous Bidirectional Neural Machine Translation." Transactions of the Association for Computational Linguistics 7 (November 2019): 91–105. http://dx.doi.org/10.1162/tacl_a_00256.

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Existing approaches to neural machine translation (NMT) generate the target language sequence token-by-token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can be produced in a right-to-left decoding direction, and thus suffers from the issue of unbalanced outputs. In this paper, we introduce a synchronous bidirectional–neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and
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Elisaye, Bekele Milke. "Bidirectional English to Wolaytta Machine Translation Using Hybrid Approach." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.B1028.15020525.

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<strong>Abstract:</strong> As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two
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Karyukin, Vladislav, Diana Rakhimova, Aidana Karibayeva, Aliya Turganbayeva, and Asem Turarbek. "The neural machine translation models for the low-resource Kazakh–English language pair." PeerJ Computer Science 9 (February 8, 2023): e1224. http://dx.doi.org/10.7717/peerj-cs.1224.

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The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages
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Milke, Elisaye Bekele, Tibebe Beshah Tesema, and Mesfin Leranso Betalo. "Bidirectional English to Wolaytta Machine Translation Using a Hybrid Approach." International Journal of Soft Computing and Engineering 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.b1028.15020525.

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As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two language models have been d
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Zhang, Zhirui, Shuangzhi Wu, Shujie Liu, Mu Li, Ming Zhou, and Tong Xu. "Regularizing Neural Machine Translation by Target-Bidirectional Agreement." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 443–50. http://dx.doi.org/10.1609/aaai.v33i01.3301443.

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Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two
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Yeong-Tsann, Phua, Navaratnam Sujata, Kang Chon-Moy, and Chew Wai-Seong. "Sequence-to-sequence neural machine translation for English-Malay." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 658–65. https://doi.org/10.11591/ijai.v11.i2.pp658-665.

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Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a conv
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B N V Narasimha Raju, Et al. "Bidirectional LSTMs with Byte Pair Encoding in NMT for CLIR using English and Telugu Parallel Corpus." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 483–89. http://dx.doi.org/10.17762/ijritcc.v11i9.8832.

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The Neural Machine Translation (NMT) is very crucial for Cross-Lingual Information Retrieval (CLIR). NMT is effective in translating English language queries to the Telugu Language. In this paper, we are translating English queries to Telugu. The NMT will utilize a parallel corpus for translations. Telugu is a resource-poor language, it is very difficult to supply large amounts of parallel corpus to NMT. So the NMT will have a problem called Out Of Vocabulary (OOV). To overcome this problem Byte Pair Encoding (BPE) is used along with Long Short Term Memory (LSTM), which segments the rare words
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Roig Allué, Blanca. "RELIABILITY AND LIMITATIONS OF GOOGLE TRANSLATE: A BILINGUAL, BIDIRECTIONAL AND GENRE-BASED EVALUATION." Entreculturas. Revista de Traducción y Comunicación Intercultural, no. 9 (February 1, 2017): 67–80. http://dx.doi.org/10.24310/entreculturasertci.vi9.11240.

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The popularity of machine translation systems (or CAT, computer-assisted translation), which enable their users to obtain automatically generated translations of any text, has been increasing ever since they were created. One of the most widely used machine translation is Google Translate, a statistical system whose performance is the object of study of this paper. In order to evaluate its reliability, a small-scale study has been carried out in which translations of tourist texts and football match reports published online generated by the tool have been analysed, and the most representative
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Zhao, Hongdan. "Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology." Journal of Electrical Systems 20, no. 6s (2024): 1751–62. http://dx.doi.org/10.52783/jes.3093.

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The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening
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Dissertations / Theses on the topic "Bidirectional Machine Translation"

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"Semi-automatic grammar induction for bidirectional machine translation." 2002. http://library.cuhk.edu.hk/record=b5895983.

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Wong, Chin Chung.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.<br>Includes bibliographical references (leaves 137-143).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Objectives --- p.3<br>Chapter 1.2 --- Thesis Outline --- p.5<br>Chapter 2 --- Background in Natural Language Understanding --- p.6<br>Chapter 2.1 --- Rule-based Approaches --- p.7<br>Chapter 2.2 --- Corpus-based Approaches --- p.8<br>Chapter 2.2.1 --- Stochastic Approaches --- p.8<br>Chapter 2.2.2 --- Phrase-spotting Approaches --- p.9<br>Chapter 2.3 --- The AT
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Hsu, Yi-Hsin, and 徐儀馨. "Bidirectional Alignment-Collocation Models for Statistical Machine Translation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/d8yh94.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>105<br>In this thesis, we propose a bidirectional alignment-collocation model (BDACM) that addresses potential problems with current SMT word alignment models. Bidirectional alignment-collocation is made possible by associating each source or target word with an alignment vector that aligns either to a neighboring word in the same language (called “collocational alignment”) or a word in the other language (called “translational alignment”). An EM algorithm is designed to jointly learn the best intra-language collocation or inter-language word alignment. With such a
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Books on the topic "Bidirectional Machine Translation"

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Huang, Xiuming. A bidirectional Chinese grammar in a machine translation system. New Mexico State University, 1986.

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Book chapters on the topic "Bidirectional Machine Translation"

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Cavalli-Sforza, Violetta, Krzysztof Czuba, Teruko Mitamura, and Eric Nyberg. "Challenges in Adapting an Interlingua for Bidirectional English-Italian Translation." In Envisioning Machine Translation in the Information Future. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39965-8_17.

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Rajeswari, K., N. Vivekanandan, Sushma Vispute, et al. "Character-Level Bidirectional Sign Language Translation Using Machine Learning Algorithms." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8129-8_18.

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Cheng, Yong. "Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning." In Springer Theses. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9748-7_5.

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Cheng, Yong. "Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation." In Springer Theses. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9748-7_2.

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Daba, Jabesa, and Yaregal Assabie. "A Hybrid Approach to the Development of Bidirectional English-Oromiffa Machine Translation." In Advances in Natural Language Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10888-9_24.

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Garg, Kamal Deep, Vandana Mohindru Sood, Sushil Kumar Narang, and Rahul Bhandari. "Bidirectional Machine Translation for Punjabi-English, Punjabi-Hindi, and Hindi-English Language Pairs." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9876-8_47.

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Tanvir, Md Tasnin, Asfia Moon Oishy, M. A. H. Akhand, and Nazmul Siddique. "Bidirectional Long-Short Term Memory with Byte Pair Encoding and Back Translation for Bangla-English Machine Translation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34619-4_38.

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Wang, Xiaozhen. "Performance and Improvement of Bidirectional Long Short-Term Memory Networks in English and Korean Machine Translation." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-8003-0_12.

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Attri, Shree Harsh, and Tarun Kumar. "The Machine Translation Systems Demystifying the Approaches." In A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815238488124020011.

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The world has many languages, each with its own unique structure in terms of vocabulary and syntax. With the rise of the Internet, communication between people from diverse cultures has become more common, necessitating the need for instantaneous translation. Since human translators cannot be available at all times for every language, the demand for effective automatic translation has grown, which should be cost-effective and immediate. Machine Translation (MT) systems aim to interpret one language into another by identifying and translating morphological inflections, Part of Speech (PoS), and
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Ulrich, Hannes, Hristina Uzunova, Heinz Handels, and Josef Ingenerf. "Proposal of Semantic Annotation for German Metadata Using Bidirectional Recurrent Neural Networks." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220474.

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The distributed nature of our digital healthcare and the rapid emergence of new data sources prevents a compelling overview and the joint use of new data. Data integration, e.g., with metadata and semantic annotations, is expected to overcome this challenge. In this paper, we present an approach to predict UMLS codes to given German metadata using recurrent neural networks. The augmentation of the training dataset using the Medical Subject Headings (MeSH), particularly the German translations, also improved the model accuracy. The model demonstrates robust performance with 75% accuracy and aim
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Conference papers on the topic "Bidirectional Machine Translation"

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Imamura, Kenji, and Eiichiro Sumita. "Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation." In Proceedings of the 7th Workshop on Asian Translation. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.wat-1.3.

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Han, Xiaoqiang, and Weizhao Zhang. "Finding Better Segmentation Granularity for Tibetan-Chinese Bidirectional Neural Machine Translation." In 2024 International Conference on Asian Language Processing (IALP). IEEE, 2024. http://dx.doi.org/10.1109/ialp63756.2024.10661111.

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Man, Ciin Zam, Si Si Mar Win, and Kyi Lai Lai Khine. "Exploring Transfer Learning in a Bidirectional Myanmar-Tedim Chin Machine Translation with the mT5 Transformer." In 2025 IEEE Conference on Computer Applications (ICCA). IEEE, 2025. https://doi.org/10.1109/icca65395.2025.11011103.

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Tang, Liang. "Optimization of English Machine Translation based on Bidirectional Encoder Representations from Transformers-Integrated Neural Architectures." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070579.

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Roy, Amit Kumar, Bipul Syam Purkayastha, and Saptarshi Paul. "A Bidirectional Statistical Machine Translation System for Exploring the Performance of the Low Resource Language Pair English-Nepali." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717590.

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Watanabe, Taro, and Eiichiro Sumita. "Bidirectional decoding for statistical machine translation." In the 19th international conference. Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1072228.1072278.

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Finch, Andrew, and Eiichiro Sumita. "Bidirectional phrase-based statistical machine translation." In the 2009 Conference. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1699648.1699658.

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Finch, Andrew, and Eiichiro Sumita. "Transliteration by bidirectional statistical machine translation." In the 2009 Named Entities Workshop: Shared Task. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1699705.1699719.

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Subramaniam, N. Venkata, N. Alwar, G. Mallikarjuna, P. Prabhakar Rao, and S. Raman. "Bidirectional machine translation in indian languages." In 2nd European Conference on Speech Communication and Technology (Eurospeech 1991). ISCA, 1991. http://dx.doi.org/10.21437/eurospeech.1991-254.

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Zhuang, Yimeng, and Mei Tu. "Pretrained Bidirectional Distillation for Machine Translation." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.63.

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