| lr,10-1-J05-4003,bq | discovering parallel sentences </term> in <term> | comparable , non-parallel corpora | </term> . We train a <term> maximum entropy | #8993 We present a novel method for discovering parallel sentences incomparable , non-parallel corpora. |
| lr,10-3-J05-4003,bq | <term> parallel data </term> from large <term> | Chinese , Arabic , and English non-parallel newspaper corpora | </term> . We evaluate the <term> quality of | #9035 Using this approach, we extract parallel data from largeChinese , Arabic , and English non-parallel newspaper corpora. |
| lr,17-6-J05-4003,bq | pairs </term> for which only scarce <term> | resources | </term> are available . In this paper we | #9117 Thus, our method can be applied with great benefit to language pairs for which only scarceresources are available. |
| lr,19-5-J05-4003,bq | scratch by starting with a very small <term> | parallel corpus | </term> ( 100,000 <term> words </term> ) and | #9087 We also show that a good-quality MT system can be built from scratch by starting with a very smallparallel corpus (100,000 words) and exploiting a large non-parallel corpus. |
| lr,29-5-J05-4003,bq | words </term> ) and exploiting a large <term> | non-parallel corpus | </term> . Thus , our method can be applied | #9097 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a largenon-parallel corpus. |
| lr,6-3-J05-4003,bq | this <term> approach </term> , we extract <term> | parallel data | </term> from large <term> Chinese , Arabic | #9031 Using this approach, we extractparallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. |
| measure(ment),3-4-J05-4003,bq | newspaper corpora </term> . We evaluate the <term> | quality of the extracted data | </term> by showing that it improves the performance | #9048 We evaluate thequality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. |
| other,11-6-J05-4003,bq | can be applied with great benefit to <term> | language pairs | </term> for which only scarce <term> resources | #9111 Thus, our method can be applied with great benefit tolanguage pairs for which only scarce resources are available. |
| other,12-2-J05-4003,bq | classifier </term> that , given a pair of <term> | sentences | </term> , can reliably determine whether | #9010 We train a maximum entropy classifier that, given a pair ofsentences, can reliably determine whether or not they are translations of each other. |
| other,22-2-J05-4003,bq | determine whether or not they are <term> | translations | </term> of each other . Using this <term> approach | #9020 We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they aretranslations of each other. |
| other,23-5-J05-4003,bq | <term> parallel corpus </term> ( 100,000 <term> | words | </term> ) and exploiting a large <term> non-parallel | #9091 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000words) and exploiting a large non-parallel corpus. |
| tech,18-4-J05-4003,bq | performance of a state-of-the-art <term> | statistical machine translation system | </term> . We also show that a good-quality | #9063 We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-artstatistical machine translation system. |
| tech,2-3-J05-4003,bq | translations </term> of each other . Using this <term> | approach | </term> , we extract <term> parallel data </term> | #9027 Using thisapproach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. |
| tech,3-2-J05-4003,bq | non-parallel corpora </term> . We train a <term> | maximum entropy classifier | </term> that , given a pair of <term> sentences | #9001 We train amaximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. |
| tech,4-1-J05-4003,bq | generation </term> . We present a novel <term> | method | </term> for <term> discovering parallel sentences | #8987 We present a novelmethod for discovering parallel sentences in comparable, non-parallel corpora. |
| tech,6-1-J05-4003,bq | present a novel <term> method </term> for <term> | discovering parallel sentences | </term> in <term> comparable , non-parallel | #8989 We present a novel method fordiscovering parallel sentences in comparable, non-parallel corpora. |
| tech,6-5-J05-4003,bq | . We also show that a good-quality <term> | MT system | </term> can be built from scratch by starting | #9074 We also show that a good-qualityMT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. |
