In this paper, we re-visit the foundations of the statistical approach to machine translation and study two forms of the Bayes decision rule: the common rule for minimizing the number of string errors and a novel rule for minimizing the number of symbol errors. The Bayes decision rule for minimizing the number of string errors is widely used, but its justification is rarely questioned. We study...

In this paper, we present a new method based on machine translation for correcting serial grammatical errors in a given sentence in learners’ writing. In our approach, translation models are generated to translate the input into a grammatical sentence. The method involves automatically learning two translation models that are based on Web-scale n-grams. The first model translates trigrams conta...

Intelligibility and fidelity are the two key notions in machine translation system evaluation, but do not always provide enough information for system development. Detailed information about the type and number of errors of each type that a translation system makes is important for diagnosing the system, evaluating the translation approach, and allocating development resources. In this paper, w...

We are developing a multilingual machine translation system to provide foreign tourists with a multilingual speech translation service in the Winter Olympic Games that will be held in Korea in 2018. For a knowledge learning to make the multilingual expansibility possible, we needed large bilingual corpus. In Korea there were a lot of Korean-English bilingual corpus, but Korean-French bilingual ...

A novel and robust approach to improving statistical machine translation fluency is developed within a minimum Bayesrisk decoding framework. By segmenting translation lattices according to confidence measures over the maximum likelihood translation hypothesis we are able to focus on regions with potential translation errors. Hypothesis space constraints based on monolingual coverage are applied...

Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards developmen...

This paper describes a Transfer-Driven Machine Translation (TDMT) system as a prototype for efficient multi-lingual spoken-dialog translation. Currently, the TDMT system deals with dialogues in the travel domain, such as travel scheduling, hotel reservation, and trouble-shooting, and covers almost all expressions presented in commercially-available travel conversation guides. In addition, to pu...

     This project investigates the relationship between lexical semantics and causative morphology in the acquisition of causative/inchoative-related verbs in English as a foreign language by Iranian speakers. Results of translation and picture judgment task show although L2 learners have largely acquired the correct lexico-syntactic classification of verbs in English, they were constrained by ...