Maitry B. Shukla

Bio: Maitry B. Shukla is an academic researcher. The author has contributed to research in topics: Machine translation. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

A Comparative Study and Analysis of Evaluation Matrices in Machine Translation

Abstract: Machine translation is a process of translating one natural language to another without much human interaction. Evaluation of any Machine Translation System (MTS) is the most important factor in a machine learning environment. There are many techniques existing to determine and optimize the quality of output in any MTS. Earlier methods are based on human judgments. Even though human evaluation methods are very much reliable, they suffer due to some disadvantages such as high cost, more time consuming and also poor re-usability. Hence, automatic methods have been proposed to reduce time and cost. In this survey, we have discussed different metrics under the automatic evaluation techniques in order to evaluate the output quality of MTS. It is believed that machine learning system developers at large would get befitted by this survey.

Machine Translation Utilizing the Frequent-Item Set Concept

Abstract: In this paper, we introduce new concepts in the machine translation paradigm. We treat the corpus as a database of frequent word sets. A translation request triggers association rules joining phrases present in the source language, and phrases present in the target language. It has to be noted that a sequential scan of the corpus for such phrases will increase the response time in an unexpected manner. We introduce the pre-processing of the bilingual corpus through proposing a data structure called Corpus-Trie (CT) that renders a bilingual parallel corpus in a compact data structure representing frequent data items sets. We also present algorithms which utilize the CT to respond to translation requests and explore novel techniques in exhaustive experiments. Experiments were performed on specific language pairs, although the proposed method is not restricted to any specific language. Moreover, the proposed Corpus-Trie can be extended from bilingual corpora to accommodate multi-language corpora. Experiments indicated that the response time of a translation request is logarithmic to the count of unrepeated phrases in the original bilingual corpus (and thus, the Corpus-Trie size). In practical situations, 5–20% of the log of the number of the nodes have to be visited. The experimental results indicate that the BLEU score for the proposed CT system increases with the size of the number of phrases in the CT, for both English-Arabic and English-French translations. The proposed CT system was demonstrated to be better than both Omega-T and Apertium in quality of translation from a corpus size exceeding 1,600,000 phrases for English-Arabic translation, and 300,000 phrases for English-French translation.