Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue

Ma, Zhiyuan; Li, Jianjun; Zhang, Zezheng; Li, Guohui; Cheng, Yongjing

doi:10.18653/v1/2021.emnlp-main.174

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Natural language understanding (NLU) is an important component of dialogue systems, including the intent classification task and the slot filling task. There are a lot of NLU methods (Goo et al 2018;Wang, Shen, and Jin 2018;Liu et al 2020;Ma et al 2021b;Rosenbaum et al 2022;Zheng et al 2023;Ma et al 2022). Qin et al directly took the output of the intent task as the input to the slot task.…”

Section: Related Work Natural Language Understandingmentioning

confidence: 99%

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

Han,

Zou,

Wang

et al. 2024

AAAI

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Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole usually suffers from gaps that exist among source domains and target domains. For instance, transferring domain-specific-knowledge-related experience is difficult. To tackle this problem, we propose a new method that explicitly decouples the transferring of general-semantic-representation-related experience and the domain-specific-knowledge-related experience. Specifically, for domain-specific-knowledge-related experience, we design two modules to capture intent-slot relation and slot-slot relation respectively. Extensive experiments on Snips and FewJoint datasets show that our method achieves state-of-the-art performance. The method improves the joint accuracy metric from 27.72% to 42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.

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Section: Related Work Natural Language Understandingmentioning

confidence: 99%

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

Han,

Zou,

Wang

et al. 2024

AAAI

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“…Other works treat KB and dialogue history equally as triplet memories (Madotto et al, 2018;Wu et al, 2019;Chen et al, 2019b;He et al, 2020a;Qin et al, 2021a). Memory networks (Sukhbaatar et al, 2015) have been applied to model the dependency between related entity triplets in KB (Bordes et al, 2017;Wang et al, 2020) and improves domain scalability (Qin et al, 2020b;Ma et al, 2021). To improve the response quality with triplet KB representation, Raghu et al (2019) proposed BOSS-NET to disentangle NLG and KB retrieval and Hong et al (2020) generated responses through a template-filling decoder.…”

Section: Triplet Representationmentioning

confidence: 99%

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

Qin,

Pan,

Chen

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website 1 , where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community. EToD Modularly EToD ( §3.1) Modularly EToD w/o PLM ( §3.1.1)

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“…Most of the previous work develops personalized (Zhang et al, 2018;Zheng et al, 2020;Song et al, 2021;Chen et al, 2023a), emotional (Ghosal et al, 2020;Zheng et al, 2023a;Deng et al, 2023c;Zheng et al, 2023b), empathetic (Rashkin et al, 2019Sabour et al, 2022) dialogue system in isolation, rather than seamlessly blending them all into one cohesive conversational flow (Smith et al, 2020;. A common approach is to predict the emotion or persona from a pre-defined set and generate the response in a multi-task manner (Ma et al, 2021;Sabour et al, 2022;. Besides that, lots of work notices these linguistic cues underneath text by directly predicting them independently as a classification task Barriere et al, 2022;Ghosh et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Wang,

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user's hidden needs and respond satisfactorily through a single-step inference. To this end, we propose a novel linguistic cue-based chain-of-thoughts (Cue-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response. To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: personality, emotion, and psychology. We conduct extensive experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings. Empirical results demonstrate our proposed Cue-CoT method outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets.

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Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue

Cited by 3 publications

References 22 publications

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

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