Class-based n-gram models of natural language
Computational Linguistics
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Unsupervised part-of-speech tagging employing efficient graph clustering
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
KenLM: faster and smaller language model queries
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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Unsupervised word clustering algorithms --- which form word clusters based on a measure of distributional similarity --- have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word clusters as factors in statistical machine translation. Although some of the language pairs in this work clearly benefit from the factor augmentation, there is no consistent improvement in translation accuracy across the board. For all language pairs, the word clusters clearly improve translation for some proportion of the sentences in the test set, but has a weak or even detrimental effect on the rest. It is shown that if one could determine whether or not to use a factor when translating a given sentence, rather substantial improvements in precision could be achieved for all of the language pairs evaluated. While such an "oracle" method is not identified, evaluations indicate that unsupervised word cluster are most beneficial in sentences without unknown words.