Emora STDM: A Versatile Framework for Innovative Dialogue System
Development
- URL: http://arxiv.org/abs/2006.06143v1
- Date: Thu, 11 Jun 2020 01:31:17 GMT
- Title: Emora STDM: A Versatile Framework for Innovative Dialogue System
Development
- Authors: James D. Finch and Jinho D. Choi
- Abstract summary: Emora STDM is a dialogue system development framework that provides novel for rapid prototyping of chat-based dialogue managers.
Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management.
- Score: 17.14709845342071
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This demo paper presents Emora STDM (State Transition Dialogue Manager), a
dialogue system development framework that provides novel workflows for rapid
prototyping of chat-based dialogue managers as well as collaborative
development of complex interactions. Our framework caters to a wide range of
expertise levels by supporting interoperability between two popular approaches,
state machine and information state, to dialogue management. Our Natural
Language Expression package allows seamless integration of pattern matching,
custom NLP modules, and database querying, that makes the workflows much more
efficient. As a user study, we adopt this framework to an interdisciplinary
undergraduate course where students with both technical and non-technical
backgrounds are able to develop creative dialogue managers in a short period of
time.
Related papers
- An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems [18.829793635104608]
We introduce a general framework for ASR in dialog systems.
We show that leveraging our new framework compared to traditional training leads to relative WER reductions of close to 10% in real-world dialog systems.
arXiv Detail & Related papers (2024-09-16T17:59:50Z) - Towards a Neural Era in Dialogue Management for Collaboration: A
Literature Survey [0.0]
Survey begins by reviewing the evolution of dialogue management paradigms in collaborative dialogue systems.
It then shifts focus to contemporary data-driven dialogue management techniques.
Paper proceeds to analyze a selected set of recent works that apply neural approaches to collaborative dialogue management.
arXiv Detail & Related papers (2023-07-18T07:20:43Z) - PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and
Compositional Experts [45.69829921539995]
This paper proposes textbfPaCE, a unified, structured, compositional multi-modal dialogue pre-training framework.
It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.
Experimental results demonstrate that PaCE achieves state-of-the-art results on eight multi-modal dialog benchmarks.
arXiv Detail & Related papers (2023-05-24T07:43:29Z) - Back to the Future: Bidirectional Information Decoupling Network for
Multi-turn Dialogue Modeling [80.51094098799736]
We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder.
BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.
Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
arXiv Detail & Related papers (2022-04-18T03:51:46Z) - UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented
Dialogues [59.499965460525694]
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.
arXiv Detail & Related papers (2021-10-15T11:56:47Z) - A Simple But Effective Approach to n-shot Task-Oriented Dialogue
Augmentation [32.43362825854633]
We introduce a framework that creates synthetic task-oriented dialogues in a fully automatic manner.
Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function.
We observe significant improvements in the fine-tuning scenarios in several domains.
arXiv Detail & Related papers (2021-02-27T18:55:12Z) - Integrating Pre-trained Model into Rule-based Dialogue Management [32.90885176553305]
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems.
Data-driven dialogue systems, usually with end-to-end structures, are popular in academic research.
We propose a method to leverage the strength of both rule-based and data-driven dialogue managers.
arXiv Detail & Related papers (2021-02-17T03:44:22Z) - A Survey on Dialog Management: Recent Advances and Challenges [72.52920723074638]
Dialog management (DM) is a crucial component in a task-oriented dialog system.
Recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance.
arXiv Detail & Related papers (2020-05-05T14:31:24Z) - Conversation Learner -- A Machine Teaching Tool for Building Dialog
Managers for Task-Oriented Dialog Systems [57.082447660944965]
Conversation Learner is a machine teaching tool for building dialog managers.
It enables dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model.
It allows dialog authors to improve the dialog manager over time by leveraging user-system dialog logs as training data.
arXiv Detail & Related papers (2020-04-09T00:10:54Z) - 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)
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.