Group sparse CNNs for question classification with answer sets
Mingbo Ma, Liang Huang, et al.
ACL 2017
We present a novel method to improve word alignment quality and eventually the translation performance by producing and combining complementary word alignments for low-resource languages. Instead of focusing on the improvement of a single set of word alignments, we generate multiple sets of diversified alignments based on different motivations, such as linguistic knowledge, morphology and heuristics. We demonstrate this approach on an English-to-Pashto translation task by combining the alignments obtained from syntactic reordering, stemming, and partial words. The combined alignment outperforms the baseline alignment, with significantly higher F-scores and better translation performance. © 2010 Association for Computational Linguistics.
Mingbo Ma, Liang Huang, et al.
ACL 2017
Bowen Zhou, Bing Xiang, et al.
SSST 2008
Mo Yu, Wenpeng Yin, et al.
ACL 2017
Jia Cui, Yonggang Deng, et al.
ASRU 2009