UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented
Dialogues
- URL: http://arxiv.org/abs/2110.08032v1
- Date: Fri, 15 Oct 2021 11:56:47 GMT
- Title: UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented
Dialogues
- Authors: Xinyan Zhao, Bin He, Yasheng Wang, Yitong Li, Fei Mi, Yajiao Liu, Xin
Jiang, Qun Liu, Huanhuan Chen
- Abstract summary: We propose a unified dialogue system (UniDS) with the two aforementioned skills.
We design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues.
We train UniDS with mixed dialogue data from a pretrained chit-chat dialogue model.
- Score: 59.499965460525694
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the advances in deep learning, tremendous progress has been made with
chit-chat dialogue systems and task-oriented dialogue systems. However, these
two systems are often tackled separately in current methods. To achieve more
natural interaction with humans, a dialogue agent needs to be capable of both
chatting and accomplishing tasks. To this end, we propose a unified dialogue
system (UniDS) with the two aforementioned skills. In particular, we design a
unified dialogue data schema, compatible for both chit-chat and task-oriented
dialogues, and we train UniDS with mixed dialogue data from a pretrained
chit-chat dialogue model. Without adding extra parameters to SOTA baselines,
UniDS can alternatively handle chit-chat and task-oriented dialogues in a
unified framework. Experimental results demonstrate that the proposed UniDS
works comparably well as the pure chit-chat system, and it outperforms
state-of-the-art task-oriented dialogue systems. More importantly, UniDS
achieves better robustness as it is able to smoothly switch between two types
of dialogues. These results demonstrate the feasibility and potential of
building an one-for-all dialogue system.
Related papers
- Adapting Text-based Dialogue State Tracker for Spoken Dialogues [20.139351605832665]
We describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11.
Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value.
arXiv Detail & Related papers (2023-08-29T06:27:58Z) - Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking [5.816391291790977]
Dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue.
Recent advances in machine reading comprehension predict both categorical and non-categorical types of slots for dialogue state tracking.
We formulate and incorporate dialogue acts, and leverage recent advances in machine reading comprehension to predict both categorical and non-categorical types of slots for dialogue state tracking.
arXiv Detail & Related papers (2022-08-04T05:18:30Z) - A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive
Conversation Systems [9.541995537438394]
We release a multi-turn dialogues dataset called CCET (Chinese Chat-Enhanced-Task)
We propose a line of fuse-motive dialogues formalization approach, along with several evaluation metrics for TOD sessions that are integrated by CC utterances.
arXiv Detail & Related papers (2022-05-12T05:43:18Z) - KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains.
We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
arXiv Detail & Related papers (2022-05-11T16:01:03Z) - User Satisfaction Estimation with Sequential Dialogue Act Modeling in
Goal-oriented Conversational Systems [65.88679683468143]
We propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction.
USDA incorporates the sequential transitions of both content and act features in the dialogue to predict the user satisfaction.
Experimental results on four benchmark goal-oriented dialogue datasets show that the proposed method substantially and consistently outperforms existing methods on USE.
arXiv Detail & Related papers (2022-02-07T02:50:07Z) - TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue
Modeling on Spoken Conversations [24.245354500835465]
We propose a novel model-agnostic data augmentation paradigm to boost the robustness of task-oriented dialogue modeling on spoken conversations.
Our approach ranked first in both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modeling on spoken conversations.
arXiv Detail & Related papers (2021-12-23T10:04:25Z) - Task-oriented Dialogue Systems: performance vs. quality-optima, a review [0.0]
State-of-the-art task-oriented dialogue systems are not yet reaching their full potential.
Other conversational quality attributes that may point to the success, or otherwise, of the dialogue, may be ignored.
This paper explores the literature on evaluative frameworks of dialogue systems and the role of conversational quality attributes in dialogue systems.
arXiv Detail & Related papers (2021-12-21T13:16:24Z) - TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented
Dialogue [113.45485470103762]
In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling.
To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
arXiv Detail & Related papers (2020-04-15T04:09:05Z) - Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
Task-oriented dialog systems are attracting more and more attention in academic and industrial communities.
We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
arXiv Detail & Related papers (2020-03-17T01:34:56Z) - Attention over Parameters for Dialogue Systems [69.48852519856331]
We learn a dialogue system that independently parameterizes different dialogue skills, and learns to select and combine each of them through Attention over Parameters (AoP)
The experimental results show that this approach achieves competitive performance on a combined dataset of MultiWOZ, In-Car Assistant, and Persona-Chat.
arXiv Detail & Related papers (2020-01-07T03:10:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.