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Measuring translation quality by testing English speakers with a new Defense Language Proficiency Test for Arabic

Published in:
Int. Conf. on Intelligence Analysis, 2-5 May 2005.

Summary

We present results from an experiment in which educated English-native speakers answered questions from a machine translated version of a standardized Arabic language test. We compare the machine translation (MT) results with professional reference translations as a baseline for the purpose of determining the level of Arabic reading comprehension that current machine translation technology enables an English speaker to achieve. Furthermore, we explore the relationship between the current, broadly accepted automatic measures of performance for machine translation and the Defense Language Proficiency Test, a broadly accepted measure of effectiveness for evaluating foreign language proficiency. In doing so, we intend to help translate MT system performance into terms that are meaningful for satisfying Government foreign language processing requirements. The results of this experiment suggest that machine translation may enable Interagency Language Roundtable Level 2 performance, but is not yet adequate to achieve ILR Level 3. Our results are based on 69 human subjects reading 68 documents and answering 173 questions, giving a total of 4,692 timed document trials and 7,950 question trials. We propose Level 3 as a reasonable nearterm target for machine translation research and development.
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Summary

We present results from an experiment in which educated English-native speakers answered questions from a machine translated version of a standardized Arabic language test. We compare the machine translation (MT) results with professional reference translations as a baseline for the purpose of determining the level of Arabic reading comprehension that...

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Measuring human readability of machine generated text: three case studies in speech recognition and machine translation

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vol. 5, ICASSP, 19-23 March 2005, pp. V-1009 - V-1012.

Summary

We present highlights from three experiments that test the readability of current state-of-the art system output from (1) an automated English speech-to-text system (2) a text-based Arabic-to-English machine translation system and (3) an audio-based Arabic-to-English MT process. We measure readability in terms of reaction time and passage comprehension in each case, applying standard psycholinguistic testing procedures and a modified version of the standard Defense Language Proficiency Test for Arabic called the DLPT*. We learned that: (1) subjects are slowed down about 25% when reading system STT output, (2) text-based MT systems enable an English speaker to pass Arabic Level 2 on the DLPT* and (3) audio-based MT systems do not enable English speakers to pass Arabic Level 2. We intend for these generic measures of readability to predict performance of more application-specific tasks.
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Summary

We present highlights from three experiments that test the readability of current state-of-the art system output from (1) an automated English speech-to-text system (2) a text-based Arabic-to-English machine translation system and (3) an audio-based Arabic-to-English MT process. We measure readability in terms of reaction time and passage comprehension in each...

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New measures of effectiveness for human language technology

Summary

The field of human language technology (HLT) encompasses algorithms and applications dedicated to processing human speech and written communication. We focus on two types of HLT systems: (1) machine translation systems, which convert text and speech files from one human language to another, and (2) speech-to-text (STT) systems, which produce text transcripts when given audio files of human speech as input. Although both processes are subject to machine errors and can produce varying levels of garbling in their output, HLT systems are improving at a remarkable pace, according to system-internal measures of performance. To learn how these system-internal measurements correlate with improved capabilities for accomplishing real-world language-understanding tasks, we have embarked on a collaborative, interdisciplinary project involving Lincoln Laboratory, the MIT Department of Brain and Cognitive Sciences, and the Defense Language Institute Foreign Language Center to develop new techniques to scientifically measure the effectiveness of these technologies when they are used by human subjects.
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Summary

The field of human language technology (HLT) encompasses algorithms and applications dedicated to processing human speech and written communication. We focus on two types of HLT systems: (1) machine translation systems, which convert text and speech files from one human language to another, and (2) speech-to-text (STT) systems, which produce...

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The effect of text difficulty on machine translation performance -- a pilot study with ILR-related texts in Spanish, Farsi, Arabic, Russian and Korean

Published in:
4th Int. Conf. on Language Resources and Evaluation, LREC, 26-28 May 2004.

Summary

We report on initial experiments that examine the relationship between automated measures of machine translation performance (Doddington, 2003, and Papineni et al. 2001) and the Interagency Language Roundtable (ILR) scale of language proficiency/difficulty that has been in standard use for U.S. government language training and assessment for the past several decades (Child, Clifford and Lowe 1993). The main question we ask is how technology-oriented measures of MT performance relate to the ILR difficulty levels, where we understand that a linguist with ILR proficiency level N is expected to be able to understand a document rated at level N, but to have increasing difficulty with documents at higher levels. In this paper, we find that some key aspects of MT performance track with ILR difficulty levels, primarily for MT output whose quality is good enough to be readable by human readers.
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Summary

We report on initial experiments that examine the relationship between automated measures of machine translation performance (Doddington, 2003, and Papineni et al. 2001) and the Interagency Language Roundtable (ILR) scale of language proficiency/difficulty that has been in standard use for U.S. government language training and assessment for the past several...

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