OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System

Jinlan Fu; Mingtao Yang; See-kiong Ng

RECORD

OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System

Mingtao Yang, See-Kiong Ng, Jinlan Fu

Computation and Language · Submitted 28 Dec 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios.

how it is stated · asserted 0.99 read the record →

Comment (0) Read on arXiv ↗

Cite ▾

arXiv ID2312.16864 (version 1)

Pith IDPR2CUT5N, Pith's permanent record of this paper and its review

CitationJinlan Fu, Mingtao Yang, See-kiong Ng. "OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System." arXiv:2312.16864 (2023). https://pith.science/paper/PR2CUT5N

Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 9,424 input tokens.

What the abstract itself states, as probabilities: claims first 0.89 · states limitation 0.30 · human participants 0.08 · controlled comparison 0.12 · randomized assignment 0.01 · states quantitative result 0.79 · compares with prior results 0.25 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue generation tasks like response generation, or both. However, the existing PCMs seldom consider dialogue comprehension tasks, such as dialogue question answering and summarization tasks. These tasks allow PCMs to glean dialogue context from various angles. This observation naturally raises the question: Can the performance of downstream dialogue tasks be enhanced if a PCM is pre-trained on dialogue management, generation, and comprehension tasks? To investigate this, we proposed an Omnipotent Dialogue pre-training model (OmniDialog). It unifies these three dialogue tasks into a monolithic framework by multi-task learning, fostering inter-task communication. The pre-training corpus of OmniDialog spans $\mathbf{7}$ dialogue-focused tasks, drawing from $\mathbf{15}$ datasets and encompassing over $\mathbf{3.2}$ million dialogue utterances. To our knowledge, OmniDialog is a pioneering PCM pre-trained across dialogue management, generation, and comprehension domains. We evaluated its performance across four tasks: dialogue summarization, end-to-end dialogue modeling, dialogue state tracking, and intent classification. The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios. Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks. Experimental results show that the OmniDialog is good at hard samples, such as long dialogues and lengthy responses.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems.	background 1.00	0.05	0.02	asserted 1.00
s1	Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue generation tasks like response generation, or both.	background 0.97	0.06	0.02	asserted 0.99
s2	However, the existing PCMs seldom consider dialogue comprehension tasks, such as dialogue question answering and summarization tasks.	gap 0.99	0.07	0.03	asserted 0.97
s3	These tasks allow PCMs to glean dialogue context from various angles.	background 0.72	0.07	0.02	asserted 0.96
s4	This observation naturally raises the question: Can the performance of downstream dialogue tasks be enhanced if a PCM is pre-trained on dialogue management, generation, and comprehension tasks?	gap 0.71	0.06	0.03	hedged possibility 0.63
s5	To investigate this, we proposed an Omnipotent Dialogue pre-training model (OmniDialog).	contribution 0.92	0.93	0.04	asserted 0.99
s6	It unifies these three dialogue tasks into a monolithic framework by multi-task learning, fostering inter-task communication.	method 0.92	0.89	0.06	asserted 1.00
s7	The pre-training corpus of OmniDialog spans $\mathbf{7}$ dialogue-focused tasks, drawing from $\mathbf{15}$ datasets and encompassing over $\mathbf{3.2}$ million dialogue utterances.	method 0.99	0.93	0.03	asserted 1.00
s8	To our knowledge, OmniDialog is a pioneering PCM pre-trained across dialogue management, generation, and comprehension domains.	contribution 0.98	0.46	0.08	asserted 0.91
s9	We evaluated its performance across four tasks: dialogue summarization, end-to-end dialogue modeling, dialogue state tracking, and intent classification.	method 0.80	0.96	0.05	asserted 0.93
s10	The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios.	result 0.80	0.92	0.48	asserted 0.99
s11	Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks.	contribution 0.51	0.92	0.03	asserted 0.79
s12	Experimental results show that the OmniDialog is good at hard samples, such as long dialogues and lengthy responses.	result 0.90	0.95	0.17	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue generation tasks like response generation, or both. However, the existing PCMs seldom consider dialogue comprehension tasks, such as dialogue question answering and summarization tasks. These tasks allow PCMs to glean dialogue context from various angles. This observation naturally raises the question: Can the performance of downstream dialogue tasks be enhanced if a PCM is pre-trained on dialogue management, generation, and comprehension tasks? To investigate this, we proposed an Omnipotent Dialogue pre-training model (OmniDialog). It unifies these three dialogue tasks into a monolithic framework by multi-task learning, fostering inter-task communication. The pre-training corpus of OmniDialog spans $\mathbf{7}$ dialogue-focused tasks, drawing from $\mathbf{15}$ datasets and encompassing over $\mathbf{3.2}$ million dialogue utterances. To our knowledge, OmniDialog is a pioneering PCM pre-trained across dialogue management, generation, and comprehension domains. We evaluated its performance across four tasks: dialogue summarization, end-to-end dialogue modeling, dialogue state tracking, and intent classification. The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios. Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks. Experimental results show that the OmniDialog is good at hard samples, such as long dialogues and lengthy responses.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 29 sentences without a confident label

Uses the method or tool of 3 papers

We follow the generation process of PPTOD [ref] .

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System · Evaluation Tasks and Datasets · 1.00
We select Adam as our optimizer [ref] with a learning rate of 5e-5 and a batch size of 128.

Adam: A Method for Stochastic Optimization · Implementation Details · 1.00
All the work is implemented with the Huggingface Library [ref] .

HuggingFace's Transformers: State-of-the-art Natural Language Processing · Implementation Details · 1.00

Uses data or values from 4 papers

Here, we consider the MultiWOZ 2.0 benchmark [ref] .

MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling · Evaluation Tasks and Datasets · 0.93
To further explore the generalization ability of OmniDialog, we evaluated domain adaptation in the dialogue summarization task on the TODSum dataset [ref] .

TODSum: Task-Oriented Dialogue Summarization with State Tracking · Domain Transfer Learning · 0.96
In this work, we select TODSum [ref] and DialogSum [ref] as our evaluation benchmarks.

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset · Evaluation Tasks and Datasets · 0.96

Compares with 24 papers

Compared to previous state-of-the-art models [ref] with comparable model size and experimental settings, OmniDialog achieves comprehensive and competitive performance in both full-dataset and low-resource settings.

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System · Introduction · 1.00
Compared to previous state-of-the-art models [ref] with comparable model size and experimental settings, OmniDialog achieves comprehensive and competitive performance in both full-dataset and low-resource settings.

Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations · Introduction · 1.00
E2E Task: the main baselines include DAMD [ref] , MinTL [ref] , DoTS [ref] , DORA [ref] , SimpleTOD [ref] , UBAR [ref] , JOUST [ref] , SOLOIST [ref] , TOP and TOP+Noisy Online Decoding (TOP+NOD) [ref] , LAVA [ref] , and PPTOD [ref] .

LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization · Baselines · 0.98

Cites as background 18 papers

JOUST [ref] pre-trains two agents by interacting with each other using reinforcement learning with structured reward functions on source domain dialogues, and further fine-tuning on target domain data.

Transferable Dialogue Systems and User Simulators · Conclusion · 0.89
PPTOD [ref] introduced a unified plug-and-play dialogue model based on T5, showing good performance in end-to-end dialogue modeling, dialogue state tracking, and intent classification tasks.

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System · Related Work · 0.89
PCMs can be divided into three categories according to pre-training tasks they considered in the pre-training phase, PCMs focus on (1) dialogue generation tasks [ref] , (2) dialogue management tasks [ref] , and (3) both dialogue generation and dialogue management tasks [ref] .

ConveRT: Efficient and Accurate Conversational Representations from Transformers · Introduction · 0.93

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.