| other,7-1-J05-4003,ak | present a novel method for discovering <term> | parallel sentences | </term> in <term> comparable , non-parallel | #8355 We present a novel method for discoveringparallel sentences in comparable, non-parallel corpora. | |
| lr,10-1-J05-4003,ak | discovering <term> parallel sentences </term> in <term> | comparable , non-parallel corpora | </term> . We train a <term> maximum entropy | #8358 We present a novel method for discovering parallel sentences incomparable , non-parallel corpora. | |
| tech,3-2-J05-4003,ak | non-parallel corpora </term> . We train a <term> | maximum entropy classifier | </term> that , given a <term> pair of sentences | #8366 We train amaximum entropy classifier that, given a pair of sentences, can reliably determinewhether or not they are translations of each other. | |
| other,10-2-J05-4003,ak | entropy classifier </term> that , given a <term> | pair of sentences | </term> , can reliably determinewhether or | #8373 We train a maximum entropy classifier that, given apair of sentences, can reliably determinewhether or not they are translations of each other. | |
| other,21-2-J05-4003,ak | reliably determinewhether or not they are <term> | translations | </term> of each other . Using this approach | #8384 We train a maximum entropy classifier that, given a pair of sentences, can reliably determinewhether or not they aretranslations of each other. | |
| other,6-3-J05-4003,ak | . Using this approach , we extract <term> | parallel datafrom | </term> large Chinese , Arabic , and English | #8395 Using this approach, we extractparallel datafrom large Chinese, Arabic, and English non-parallel newspaper corpora. | |
| lr,15-3-J05-4003,ak | large Chinese , Arabic , and English <term> | non-parallel newspaper corpora | </term> . We evaluate the qualityof the extracted | #8404 Using this approach, we extract parallel datafrom large Chinese, Arabic, and Englishnon-parallel newspaper corpora. | |
| tech,17-4-J05-4003,ak | performance of a state-of-the-art <term> | statisticalmachine translation system | </term> . We also show that a good-quality | #8425 We evaluate the qualityof the extracted data by showing that it improves the performance of a state-of-the-artstatisticalmachine translation system. | |
| tech,6-5-J05-4003,ak | . We also show that a good-quality <term> | MT system | </term> can be built fromscratch by starting | #8435 We also show that a good-qualityMT system can be built fromscratch by starting with a very small parallel corpus (100,000 words) and exploiting a largenon-parallel corpus. | |
| lr,18-5-J05-4003,ak | fromscratch by starting with a very small <term> | parallel corpus | </term> ( 100,000 words ) and exploiting | #8447 We also show that a good-quality MT system can be built fromscratch by starting with a very smallparallel corpus (100,000 words) and exploiting a largenon-parallel corpus. | |
| lr,28-5-J05-4003,ak | and exploiting a largenon-parallel <term> | corpus | </term> . Thus , our method can be applied | #8457 We also show that a good-quality MT system can be built fromscratch by starting with a very small parallel corpus (100,000 words) and exploiting a largenon-parallelcorpus. | |
| other,11-6-J05-4003,ak | can be applied with great benefit to <term> | language pairs | </term> forwhich only scarce resources are | #8470 Thus, our method can be applied with great benefit tolanguage pairs forwhich only scarce resources are available. |
