| tech,10-1-H01-1041,ak | been developing a <term> Korean-to-English | machine | translation system CCLINC ( Common Coalition | #397 At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory). | |
| tech,30-1-H01-1042,ak | </term> , to the <term> output </term> of <term> | machine | translation ( MT ) systems </term> . We believe | #574 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output ofmachine translation (MT) systems. | |
| tech,24-2-H01-1042,ak | process </term> and the development of <term> | machine | translation systems </term> . This , the | #605 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development ofmachine translation systems. | |
| other,16-6-H01-1042,ak | from duplicating the experiment using <term> | machine | translation output </term> . Subjects were | #678 We tested this to see if similar criteria could be elicited from duplicating the experiment usingmachine translation output. | |
| other,11-8-H01-1042,ak | human translations </term> , others were <term> | machine | translation outputs </term> . The subjects | #708 Some of the extracts were expert human translations, others weremachine translation outputs. | |
| other,24-9-H01-1042,ak | expert human translation </term> or a <term> | machine | translation </term> . Additionally , they | #736 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or amachine translation. | |
| tech,28-4-H01-1055,ak | </term> can be overcome by employing <term> | machine | learning techniques </term> . In this paper | #1023 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employingmachine learning techniques. | |
| tech,5-1-P01-1070,ak | </term> . We describe a set of <term> supervised | machine | learning experiments </term> centering on | #2131 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. | |
| tech,8-1-N03-1004,ak | of <term> ensemble methods </term> in <term> | machine | learning </term> and other areas of <term> | #2315 Motivated by the success of ensemble methods inmachine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. | |
| tech,11-1-N03-2036,ak | unigram model </term> for <term> statistical | machine | translation </term> that uses a much simpler | #3402 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. | |
| tech,6-2-P03-1050,ak | model </term> is based on <term> statistical | machine | translation </term> and it uses an <term> English | #4455 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. | |
| tech,16-1-H05-1005,ak | input to correct <term> errors </term> in <term> | machine | translation </term> and thus improve the | #5167 In this paper, we use the information redundancy in multilingual input to correct errors inmachine translation and thus improve the quality of multilingual summaries. | |
| tech,6-4-H05-1005,ak | documents . Further , the use of multiple <term> | machine | translation systems </term> provides yet | #5228 Further, the use of multiplemachine translation systems provides yet more redundancy, yielding different ways to realize that information in English. | |
| other,6-5-H05-1005,ak | demonstrate how <term> errors </term> in the <term> | machine | translations </term> of the input Arabic | #5252 We demonstrate how errors in themachine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases. | |
| tech,13-2-H05-1012,ak | training material </term> for problems in <term> | machine | translation </term> and that a mixture of | #5305 We demonstrate that it is feasible to create training material for problems inmachine translation and that a mixture of supervised and unsupervised methods yields superior performance. | |
| measure(ment),15-4-H05-1012,ak | as well as improvement on several <term> | machine | translation tests </term> . Performance of | #5348 Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests. | |
| tech,4-1-H05-1095,ak | presents a <term> phrase-based statistical | machine | translation method </term> , based on <term> | #5587 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. | |
| tech,20-1-H05-1101,ak | been adopted in the literature on <term> | machine | translation </term> . These <term> models </term> | #5698 This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature onmachine translation. | |
| tech,8-1-H05-1117,ak | <term> automatic evaluation </term> of <term> | machine | translation </term> and <term> document summarization | #5914 Following recent developments in the automatic evaluation ofmachine translation and document summarization, we present a similar approach, implemented in a measure called POURPRE, for automatically evaluating answers to definition questions. | |
| tech,12-2-H05-2007,ak | tool </term> intended for developers of <term> | machine | translation systems </term> , and demonstrate | #6057 We incorporate this analysis into a diagnostic tool intended for developers ofmachine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output. |
