The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2
Open accessAbstract
The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal.The traditional approach to building such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually.However, such an optimization scheme does not necessarily yield an overall performance improvement of the whole system.On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus.This scheme makes it difficult for goal-oriented dialogues where the system needs to be integrated with external systems or to provide interpretable information about why the system generated a particular response.In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above.Our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287 in human evaluations, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., & Gašić, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1547 · Cited by 1,028
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Weston, J., Dinan, E., & Miller, A. (2018) · Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI · doi:10.18653/v1/w18-5713 · Cited by 165
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Recent advances in deep learning based dialogue systems: a systematic survey
Ni, J., Young, T., Pandelea, V., Xue, F., & Cambria, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10248-8 · Cited by 264
Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168
CodeFill: multi-token code completion by jointly learning from structure and naming sequences
Izadi, M., Gismondi, R., & Gousios, G. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510172 · Cited by 86
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
Behnia, R., Ebrahimi, M. R., Pacheco, J., & Padmanabhan, B. (2022) · 2022 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw58026.2022.00078 · Cited by 60
Chen, D., Chen, H., Yang, Y., Lin, A., & Yu, Z. (2021) · arXiv · doi:10.48550/arxiv.2104.00783 · Cited by 6
Metadata
- Authors
- Ham, D., Lee, J.-G., Jang, Y., & Kim, K.-E.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 583-592
- OpenAlex
- W3034533785
- Cited by
- 161
- References
- 4
- FWCI
- 13.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Ham, D., Lee, J.-G., Jang, Y., & Kim, K.-E. (2020). End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.54
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