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Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages.

, , , , , , , , and . IEEE Trans. Speech Audio Process., 19 (6): 1569-1583 (2011)

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Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages., , , , , , , , and . IEEE Trans. Speech Audio Process., 19 (6): 1569-1583 (2011)Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned?, , , and . EMNLP (Industry Track), page 121-127. Association for Computational Linguistics, (2022)Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances., , , , and . COLING (Industry), page 21-32. International Committee on Computational Linguistics, (2020)A Comparison of Various Types of Extended Lexicon Models for Statistical Machine Translation., , , and . AMTA, Association for Machine Translation in the Americas, (2010)(Hidden) Conditional Random Fields Using Intermediate Classes for Statistical Machine Translation., , , , and . MTSummit, (2013)Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding., and . EMNLP (1), page 1190-1198. Association for Computational Linguistics, (2021)Predicting Temporal Performance Drop of Deployed Production Spoken Language Understanding Models., , , and . Interspeech, page 1249-1253. ISCA, (2021)The Impact of Intent Distribution Mismatch on Semi-Supervised Spoken Language Understanding., , , and . Interspeech, page 4708-4712. ISCA, (2021)