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Learning to Embed Semantic Correspondence for Natural Language Understanding.

, , and . CoNLL, page 131-140. Association for Computational Linguistics, (2018)

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Data-driven user simulation for automated evaluation of spoken dialog systems., , , , and . Comput. Speech Lang., 23 (4): 479-509 (2009)Multitask Pointer Network for Korean Dependency Parsing., , and . ACM Trans. Asian & Low-Resource Lang. Inf. Process., 18 (3): 24:1-24:10 (2019)Exploring Domain Robust Lightweight Reward Models based on Router Mechanism., , , and . ACL (Findings), page 8644-8652. Association for Computational Linguistics, (2024)Three phase verification for spoken dialog clarification., , and . IUI, page 55-61. ACM, (2006)Hybrid user intention modeling to diversify dialog simulations., , , , and . Comput. Speech Lang., 25 (2): 307-326 (2011)Controllable Text Generation Using Semantic Control Grammar., , , , , and . IEEE Access, (2023)Learning to Embed Semantic Correspondence for Natural Language Understanding., , and . CoNLL, page 131-140. Association for Computational Linguistics, (2018)Hybrid Approach to User Intention Modeling for Dialog Simulation., , , and . ACL/IJCNLP (2), page 17-20. The Association for Computer Linguistics, (2009)Speech recognition error correction using maximum entropy language model., , and . INTERSPEECH, page 2137-2140. ISCA, (2004)Semantic Vector Learning and Visualization with Semantic Cluster Using Transformers in Natural Language Understanding.. J. Comput. Sci. Eng., 16 (2): 63-78 (2022)