BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The class imbalance problem: A systematic study
Intelligent Data Analysis
Domain adaptation for statistical machine translation with domain dictionary and monolingual corpora
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Streaming for large scale NLP: language modeling
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Stream-based randomised language models for SMT
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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We consider using online language models for translating multiple streams which naturally arise on the Web. After establishing that using just one stream can degrade translations on different domains, we present a series of simple approaches which tackle the problem of maintaining translation performance on all streams in small space. By exploiting the differing throughputs of each stream and how the decoder translates prior test points from each stream, we show how translation performance can equal specialised, per-stream language models, but do this in a single language model using far less space. Our results hold even when adding three billion tokens of additional text as a background language model.