DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations (2024.findings-naacl)
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| Challenge: | Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context. |
| Approach: | They propose a model that pre-trains LLMs to learn diverse task-oriented dialogue representations by removing domain knowledge that contradicts TODs. |
| Outcome: | The proposed model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues. |
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| Challenge: | Existing pre-trained language models lack diversity and linguistic challenges in task-oriented dialogues. |
| Approach: | They propose a self-bootstrapping dialogue pre-training model called BootTOD . it learns task-oriented dialogue representations via a framework . |
| Outcome: | The proposed model outperforms strong TOD baselines on diverse dialogue tasks. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
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TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue (2020.emnlp-main)
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| Challenge: | Existing pre-trained language models with self-attention encoder architectures are less useful in practice. |
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An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
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| Outcome: | The proposed model outperforms existing models and improves their performance. |
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
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| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
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Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)
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| Challenge: | Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task . |
| Approach: | They propose to add a further pre-training phase to the model to improve downstream tasks . they propose to use a domain-adaptive pre-tuning phase to fine-tune the models on unlabeled data . |
| Outcome: | The proposed method improves multiple task-oriented dialogue downstream tasks. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
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TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Contrastive Learning for Task-Independent SpeechLLM-Pretraining (2025.findings-acl)
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| Challenge: | Large language models excel in speech processing tasks but their reliance on written text limits their application in real-world scenarios. |
| Approach: | They propose a task-independent speech pretraining stage and task-specific fine-tuning stage to adapt LLMs to speech processing tasks. |
| Outcome: | The proposed model outperforms models specialized on speech translation and question answering while being trained on 10% of the task-specific data. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
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