Machine Translation And Computer Assisted Translation

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Teritaa

Technology

Machine Translation and Computer-Assisted Translation: a New Way of Translating

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Curious to learn more about how much a translator could really benefit from this daunting combination, Cris Silva and Giovana Boselli conducted an experiment in which we combined machine translation and translation memory. This slide discusses our process and statistics in an attempt to provide translation and localization professionals with some empirical information on the combined use of machine translation and computer-assisted translation.

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Our talk at CHI2015 in Seoul, South Korea. Find more information at www.kotarohara.com . YouTube: https://youtu.be/isqsYLkX9gA Makeability Lab: http://www.cs.umd.edu/~jonf/ Microsoft Research: http://research.microsoft.com/ ABSTRACT Language barrier is the primary challenge for effective cross-lingual conversations. Spoken language translation (SLT) is perceived as a cost-effective alternative to less affordable human interpreters, but little research has been done on how people interact with such technology. Using a prototype translator application, we performed a formative evaluation to elicit how people interact with the technology and adapt their conversation style. We conducted two sets of studies with a total of 23 pairs (46 participants). Participants worked on storytelling tasks to simulate natural conversations with 3 different interface settings. Our findings show that collocutors naturally adapt their style of speech production and comprehension to compensate for inadequacies in SLT. We conclude the paper with the design guidelines that emerged from the analysis.

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Computer Aided Translation Training System (CATS) provides a package solutions to the problems of translation translation. CATS combines artificial intelligence, data collection, and visualization of information technology, which makes the translation teaching, class management and monitoring on one single platform areality. Translation and interpretaton teaching resources on CATS are updated regularly into detailed categories, making the teaching materials easy to access. CATS supports translation and interpretation teaching and practices, company internships as well as scientific research.

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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, there is no benchmark parallel Amharic-Arabic text corpora available for Machine Translation task. Therefore, a small parallel Quranic text corpus is constructed by modifying the existing monolingual Arabic text and its equivalent translation of Amharic language text corpora available on Tanzile. Experiments are carried out on Two Long ShortTerm Memory (LSTM) and Gated Recurrent Units (GRU) based Neural Machine Translation (NMT) using Attention-based Encoder-Decoder architecture which is adapted from the open-source OpenNMT system. LSTM and GRU based NMT models and Google Translation system are compared and found that LSTM based OpenNMT outperforms GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%, and 6% respectively.

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The work in the area of machine translation has been going on for last few decades but the promising translation work began in the early 1990s due to advanced research in Artificial Intelligence and Computational Linguistics. India is a multilingual and multicultural country with over 1.25 billion population and 22 constitutionally recognized languages which are written in 12 different scripts. This necessitates the automated machine translation system for English to Indian languages and among Indian languages so as to exchange the information amongst people in their local language. Many usable machine translation systems have been developed and are under development in India and around the world. The paper focuses on different approaches used in the development of Machine Translation Systems and also briefly described some of the Machine Translation Systems along with their features, domains and limitations.

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