By Sungdai Cho, Hyo Sang Lee, Hye-Sook Wang
This textbook is the 9th quantity in a five-level sequence (Beginning 1 and a pair of, Intermediate 1 and a couple of, complicated Intermediate 1 and a pair of, complicated 1 and a pair of, and excessive complicated 1 and a couple of) constructed collaboratively by way of prime lecture room lecturers and linguists of Korean. All sequence’ volumes were constructed according to performance-based ideas and methodology—contextualization, learner-centeredness, use of real fabrics, function/task-orientedness, stability among ability getting and ability utilizing, and integration of talking, listening, examining, writing, and tradition. Grammar issues are systematically brought with basic yet enough motives and plentiful examples, routines, and drills.
Each lesson of this quantity contains pre-reading actions, one or major analyzing texts, new phrases, worthy expressions, routines, comprehension questions, comparable examining, dialogue and composition, and English translation of the analyzing texts.
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Additional info for Integrated Korean: High Advanced 1
Adding morphology to EBMT for Arabic-to-English translation We turn our attention now to the core topic of this chapter, how morphology can be used to improve EBMT for Arabic to English translation. We reiterate the data sparseness challenge faced by data-driven systems in general, and in translating Using morphology to improve Example-Based Machine Translation from Arabic in particular. Bar & Dershowitz (Chapter 4) reproduce for Arabic the results previously obtained for Spanish by Callison-Burch (2007): while the percentage of word sequences (n-grams) covered increases with corpus size, even with very large corpora most sequences longer than one word remain uncovered.
1995), the first large scale publicly available parallel corpus. SMT systems require large parallel corpora in order to estimate accurately the parameters of the underlying statistical models and, to the extent that larger corpora mean more l anguage coverage, the larger the better Callison-Burch (2007). g. Brown 1996). The final reason for choosing EBMT might just be that there is no reason for everybody to pursue a single MT approach. While EBMT has not been a popular approach in the United States – the only research group working with that approach was indeed ours – it has received substantially more attention in Europe and Japan, among other places.
Our work addresses specifically this issue: we aim to transform the input to be translated into a more general class whose membership includes larger portions of the training corpus, so that the system has more examples from which to choose the best translation. This strategy increases the coverage of the available examples and captures things that are not directly seen in the text. Even if an exact translation cannot be found in the corpus, a generalized translation can still be largely correct and is superior to no translation at all.