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Exploiting long context using joint distance and occurrence information for language modeling

. Nanyang Technological University, Singapore, (2018)

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TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling., , , and . CoRR, (2021)Modeling of term-distance and term-occurrence information for improving n-gram language model performance., , , and . ACL (2), page 233-237. The Association for Computer Linguistics, (2013)On the study of very low-resource language keyword search., , , , , , and . APSIPA, page 358-364. IEEE, (2015)The development and analysis of a Malay broadcasr news corpus., , , , , , , and . O-COCOSDA/CASLRE, page 1-5. IEEE, (2013)Improving language modeling by using distance and co-occurrence information of word-pairs and its application to LVCSR., , , and . ICASSP, page 4883-4887. IEEE, (2014)TDTO language modeling with feedforward neural networks., , , and . INTERSPEECH, page 1458-1462. ISCA, (2015)An Empirical Evaluation of Stop Word Removal in Statistical Machine Translation., , and . ESIRMT/HyTra@EACL, page 30-37. Association for Computational Linguistics, (2012)Multi-space random mapping for speaker identification., , , and . IEICE Electron. Express, 2 (7): 226-231 (2005)Decoupling Word-Pair Distance and Co-occurrence Information for Effective Long History Context Language Modeling., , , and . IEEE ACM Trans. Audio Speech Lang. Process., 23 (7): 1221-1232 (2015)Exploiting long context using joint distance and occurrence information for language modeling. Nanyang Technological University, Singapore, (2018)