Papers with end-to-end task-oriented dialog system
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022.findings-naacl)
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| Challenge: | Existing task-oriented dialog systems suffer from error propagation from inaccurate dialog states and responses. |
| Approach: | They propose a back reconstruction approach for end-to-end task-oriented dialog system . they use back reconstruction to reconstruct the original input context from the generated dialog states . |
| Outcome: | Extensive experiments on MultiWOZ 2.0 and CamRest676 show the effectiveness of the proposed model. |
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue (2021.emnlp-main)
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| Challenge: | Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. |
| Approach: | They propose a mechanism to model deterministic entity knowledge by using an intention reasoning network to obtain intention-aware representations of conceptual tokens. |
| Outcome: | The proposed mechanism captures concept shifts and generates accurate responses on two representative multi-domain dialog datasets. |
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020.acl-main)
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| Challenge: | Recent studies show remarkable success in end-to-end task-oriented dialog systems . however, most models rely on large training data, which is difficult to scalable for new domains with limited labeled data. |
| Approach: | They propose a shared-private network which exploits the relevance between the target domain and each domain. |
| Outcome: | The proposed model outperforms existing methods on multi-domain dialogue by 13.9% on average. |
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |