Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao
| Challenge: | ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
| Approach: | They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments. |
| Outcome: | The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
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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. |
End-to-End Learning of Task-Oriented Dialogs (N18-4)
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| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
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End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
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| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
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| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
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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. |
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)
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| Challenge: | Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results . |
| Approach: | They combine paradigm of leveraging large pre-trained language models with multi-task learning framework . their model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 . |
| Outcome: | The proposed model achieves state-of-the-art results on multiWOZ 2.0 and MultiWOZ 2.1 . it also improves generalization capability through domain adaptation experiments in the few-shot setting. |
Domain-specific Attention with Distributional Signatures for Multi-Domain End-to-end Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Existing methods to construct multi-domain task-oriented dialogue systems are difficult to extend to new domains due to high cost of data annotation and scarcity of labeled dialogue data. |
| Approach: | They propose a domain attention module that uses distributional signatures to construct multi-domain dialogue systems with limited data. |
| Outcome: | The proposed method outperforms baseline models on most metrics while keeping smaller model scale. |
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
| Approach: | They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity . |
| Outcome: | This paper provides a brief survey of the main challenges of traditional approaches in speech translation . it reveals that many end-to-end models fail due to compromises made to address data scarcity. |