By Takako Aikawa, Kentaro Yamamoto, Hitoshi Isahara (auth.), Hitoshi Isahara, Kyoko Kanzaki (eds.)
This publication constitutes the refereed complaints of the eighth foreign convention on Advances in common Language Processing, JapTAL 2012, Kanazawa, Japan, in October 2012.
The 27 revised complete papers and five revised brief papers offered have been conscientiously reviewed and chosen from forty two submissions. The papers are prepared in topical sections on laptop translation, multilingual matters, resouces, semantic research, sentiment research, in addition to speech and generation.
Read Online or Download Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings PDF
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Extra info for Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings
Statistical machine translation (SMT) systems depend on the availability of domain-speciﬁc bilingual parallel text. However parallel corpora are a limited resource and they are often not available for some domains or language pairs. We analyze the feasibility of extracting parallel sentences from multimodal comparable corpora. This work extends the use of comparable corpora by using audio sources instead of texts on the source side. The audio is transcribed by an automatic speech recognition system and translated with a baseline SMT system.
One can observe that the BLEU score of the adapted system matches the one of Exp1 in most of the cases. Therefore, we conclude that the errors induced by the SMT system have no major impact on the performance of the parallel sentence extraction algorithm. These ﬁndings are in line with those of . These results show that the choice of the appropriate TER threshold depends on the type of data. 93. In Exp1, we use the reference translations as query and IR should in theory ﬁnd all the sentences in the large corpus with a TER of zero.
To resolve this problem, we need to examine and classify diﬀerent kinds of wrong descriptions and correct them manually. Cause 3. We omitted the case for failing morphological analysis. 36 J. Sakata, M. Tokuhisa, and J. Murakami Human Evaluation. Table 2 shows evaluation criteria. Table 2. Evaluation criteria Eval. 1 The sentence structure is correctly composed, and all local translation are not mistranslation. Eval. 2 The sentence structure is correctly composed, but a local translation is mistranslation.
Advances in Natural Language Processing: 8th International Conference on NLP, JapTAL 2012, Kanazawa, Japan, October 22-24, 2012. Proceedings by Takako Aikawa, Kentaro Yamamoto, Hitoshi Isahara (auth.), Hitoshi Isahara, Kyoko Kanzaki (eds.)