Related papers: Dataflow Dialogue Generation

Dataflow Dialogue Generation

URL: http://arxiv.org/abs/2308.02323v1
Date: Fri, 4 Aug 2023 13:40:54 GMT
Title: Dataflow Dialogue Generation
Authors: Joram Meron, Victor Guimar\~aes
Abstract summary: We show an example of agenda driven dialogue generation for the MultiWOZ domain, and an example of generation without an agenda for the SMCalFlow domain. We show an improvement in the accuracy of the translation of user requests to dataflow expressions when the generated dialogues are used to augment the translation training dataset.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We demonstrate task-oriented dialogue generation within the dataflow dialogue paradigm. We show an example of agenda driven dialogue generation for the MultiWOZ domain, and an example of generation without an agenda for the SMCalFlow domain, where we show an improvement in the accuracy of the translation of user requests to dataflow expressions when the generated dialogues are used to augment the translation training dataset.

Related papers

DFlow: Diverse Dialogue Flow Simulation with Large Language Models [16.209331014315463]
This paper proposes a novel data augmentation method designed to enhance the diversity of synthetic dialogues. We generate a task-oriented dialogue dataset comprising 3,886 dialogue flows across 15 different domains.
arXiv Detail & Related papers (2024-10-18T20:35:28Z)
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation [12.93942316816741]
GPT-4 is used to simulate the user and agent interaction, generating thousands of annotated dialogues with DST labels. A two-stage fine-tuning on LLaMA 2 is performed on the generated data and the real data for the DST prediction. Our approach is also capable of adapting to the dynamic demands in real-world scenarios, generating dialogues in new domains swiftly.
arXiv Detail & Related papers (2024-05-17T07:00:05Z)
Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts [10.829227084902428]
We investigate the feasibility and effectiveness of Large Language Models (LLMs)-based data generation in source-grounded information-seeking dialogs. We create MISeD -- Meeting Information Seeking Dialogs dataset -- a dataset of information-seeking dialogs focused on meeting transcripts. Finetuning on MISeD gives comparable response generation quality to finetuning on fully manual data, while improving attribution quality and reducing time and effort.
arXiv Detail & Related papers (2024-05-02T09:35:06Z)
Contextual Data Augmentation for Task-Oriented Dialog Systems [8.085645180329417]
We develop a novel dialog augmentation model that generates a user turn, conditioning on full dialog context. With a new prompt design for language model, and output re-ranking, the dialogs generated from our model can be directly used to train downstream dialog systems.
arXiv Detail & Related papers (2023-10-16T13:22:34Z)
Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation [50.06143883455979]
Flowchart-grounded troubleshooting dialogue (FTD) systems follow the instructions of a flowchart to diagnose users' problems in specific domains. We propose a plan-based synthetic data generation approach that generates diverse synthetic dialog data at scale.
arXiv Detail & Related papers (2023-05-02T11:08:27Z)
Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
arXiv Detail & Related papers (2022-08-16T08:21:12Z)
Task-Oriented Dialogue as Dataflow Synthesis [158.77123205487334]
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people.
arXiv Detail & Related papers (2020-09-24T00:35:26Z)
Modeling Long Context for Task-Oriented Dialogue State Generation [51.044300192906995]
We propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model. Our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.
arXiv Detail & Related papers (2020-04-29T11:02:25Z)
Paraphrase Augmented Task-Oriented Dialog Generation [68.1790912977053]
We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels.
arXiv Detail & Related papers (2020-04-16T05:12:36Z)
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation [59.174903564894954]
In this work, we extend this approach to the task of dialog state tracking for goal-oriented dialogs. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs. Experiments on various dialog datasets show that our model improves the downstream dialog trackers' robustness via generative data augmentation.
arXiv Detail & Related papers (2020-01-23T15:34:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.