machine translation 2020


Machine Translation is software that automatically renders text from one language into another. A key benefit of RBMT over other MT approaches is that it can produce better quality for language pairs with very different word orders

Neural Machine Translation with Joint Representation
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Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, eg, alignment, the recent Neural Machine Translation (NMT) systems resort to the attention

The Integrated Evaluation Method of Machine Translation Quality Based on Z-numbers.
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The evaluation of machine translations is of great significance to machine translation research. The evaluations are based on the judgments of the evaluators, and it is necessary for us to ensure that the judgments are reliable. Since the Z-number has the potential to

Optimizing segmentation granularity for neural machine translation
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In neural machine translation (NMT), it has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair encoding (BPE) and its variants are the predominant approach to generating these

Teaching Arabic Machine Translation to EFL Student Translators: A Case Study of Omani Translation Undergraduates
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The present paper describes a machine translation (MT) course taught to undergraduate students in the Department of English Language and Literature at Dhofar University in Oman. The course is one of the major requirements for BA in Translation . Fifteen EFL

Multi-word verbs in English to Finnish machine translation I: Single-word to multi-word verb mapping
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The report discusses such cases, where a single-word English verb maps to a multiword Finnish verb. Translation to Finnish includes many problems, because the multi-word solution in translation affects the inflection of the modifiers of the verb. The translation system

MACHINE TRANSLATION YESTERDAY, TODAY AND TOMORROW
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BIAS IN MACHINE TRANSLATION
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Page 1. BIAS IN MACHINE TRANSLATION Philipp Wiesenbach January 2020 1 Page 2. OVERVIEW 2 . 1 Page 3. 1. Recap of bias forms 2. Examples of bias in MT 3. Learning gender-neutral word embeddings 4. Examining impact of embeddings in MT: GloVe Hard-GloVe

Portuguese-Chinese Neural Machine Translation
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This work reports on a study addressing Neural Machine Translation for the language pair Portuguese↔ Chinese where the development of a state-of-the-art Machine Translation system for this pair was undertaken by using only freely available resources. There are two

Neural Machine Translation Improvement by Acoustic Embedding
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Neural machine translation (NMT) has successfully redefined the state of the art in machine translation on several language pairs. One popular framework models the translation process end-to-end using attentional encoder-decoder architecture and treats each word in

Curriculum Learning for Neural Machine Translation
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Curriculum learning (CL) is a training framework from Reinforcement Learning (RL) in which easy training examples are initially seen and the difficulty of examples are gradually increased over time. In this project, we aim to (1) reproduce CL for neural machine

INCORPORATING BERT INTO NEURAL MACHINE TRANSLATION
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The recently proposed BERT (Devlin et al.) has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks

NEURAL MACHINE TRANSLATION WITH UNIVERSAL VISUAL REPRESENTATION
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Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we present a universal

CONSTRUCTION OF AMHARIC-ARABIC PARALLEL TEXT CORPUS FOR NEURAL MACHINE TRANSLATION
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Many automatic translation works have been addressed between major European language pairs, by taking advantage of large scale parallel corpora, but very few research works are conducted on the Amharic-Arabic language pair due to its parallel data scarcity. However

Retrieving Music Semantics from Optical Music Recognition by Machine Translation
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We present a machine learning-based approach to retrieve the semantics of a sequence of (graphic) music symbols, which constitutes a central problem in the field of Optical Music Recognition (OMR). OMR is the process of converting the digital image of a score into a

Can You Translate that into Man Commercial Machine Translation Systems Include Stylistic Biases
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The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make

Exploring Human-like Learning Capabilities of Neural Machine Translation
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Neural networks became the dominant approach to solving many statistical modeling problems, reaching or surpassing human-level performance on several tasks. Even though they were originally inspired by the neural interactions inside the biological brain, their

Chinese to English Machine Translation Specialized in Game of Go and Cooking Recipe with Limited Data
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In this project, we built an RNN with Attention model for machine translation from Chinese to English on two special tracks: Chinese cooking recipe and Game of Go. To evaluate our translation results in these two tracks, we also proposed a new evaluation criteria BLEU

TEXTSHIELD: Robust Text Classification Based on Multimodal Embedding and Neural Machine Translation
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Text-based toxic content detection is an important tool for reducing harmful interactions in online social media environments. Yet, its underlying mechanism, deep learning-based text classification (DLTC), is inherently vulnerable to maliciously crafted adversarial texts. To

Analyzing the Problems of Vocabulary in Japanese-Chinese Neural Network Machine Translation
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Abstract In recent years, Neural Network Machine Translation (NMT) has made great progress as a new translation technology. Its translation results are not only more accurate but also more fluid. But at the same time, NMT also has many problems that need to be

A Path for Translation of Machine Learning Products into Healthcare Delivery
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Despite enormous enthusiasm, machine learning models are rarely translated into clinical care and there is minimal evidence of clinical or economic impact. New conference venues and academic journals have emerged to promote the proliferating research; however, the


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