Quick Starting Dialog Systems with Paraphrase Generation
- URL: http://arxiv.org/abs/2204.02546v1
- Date: Wed, 6 Apr 2022 02:35:59 GMT
- Title: Quick Starting Dialog Systems with Paraphrase Generation
- Authors: Louis Marceau, Raouf Belbahar, Marc Queudot, Eric Charton, Marie-Jean
Meurs
- Abstract summary: We propose a method to reduce the cost and effort of creating new conversational agents by artificially generating more data from existing examples.
Our proposed approach can kick-start a dialog system with little human effort, and brings its performance to a level satisfactory enough for allowing actual interactions with real end-users.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Acquiring training data to improve the robustness of dialog systems can be a
painstakingly long process. In this work, we propose a method to reduce the
cost and effort of creating new conversational agents by artificially
generating more data from existing examples, using paraphrase generation. Our
proposed approach can kick-start a dialog system with little human effort, and
brings its performance to a level satisfactory enough for allowing actual
interactions with real end-users. We experimented with two neural paraphrasing
approaches, namely Neural Machine Translation and a Transformer-based seq2seq
model. We present the results obtained with two datasets in English and in
French:~a crowd-sourced public intent classification dataset and our own
corporate dialog system dataset. We show that our proposed approach increased
the generalization capabilities of the intent classification model on both
datasets, reducing the effort required to initialize a new dialog system and
helping to deploy this technology at scale within an organization.
Related papers
- DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization [127.714919036388]
DIONYSUS is a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Our experiments show that DIONYSUS outperforms existing methods on six datasets.
arXiv Detail & Related papers (2022-12-20T06:21:21Z) - A Model-Agnostic Data Manipulation Method for Persona-based Dialogue
Generation [107.82729587882397]
It is expensive to scale up current persona-based dialogue datasets.
Each data sample in this task is more complex to learn with than conventional dialogue data.
We propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model.
arXiv Detail & Related papers (2022-04-21T03:49:54Z) - Structure Extraction in Task-Oriented Dialogues with Slot Clustering [94.27806592467537]
In task-oriented dialogues, dialogue structure has often been considered as transition graphs among dialogue states.
We propose a simple yet effective approach for structure extraction in task-oriented dialogues.
arXiv Detail & Related papers (2022-02-28T20:18:12Z) - Transferable Dialogue Systems and User Simulators [17.106518400787156]
One of the difficulties in training dialogue systems is the lack of training data.
We explore the possibility of creating dialogue data through the interaction between a dialogue system and a user simulator.
We develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents.
arXiv Detail & Related papers (2021-07-25T22:59:09Z) - Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation [16.90730526207747]
We present a transformer-based model for multi-turn dialog response generation.
Our solution is based on a hybrid approach which augments a transformer-based generative model with a novel retrieval mechanism.
arXiv Detail & Related papers (2021-05-19T16:34:33Z) - 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) - Data-Efficient Methods for Dialogue Systems [4.061135251278187]
Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa.
Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts.
In this thesis, we introduce a series of methods for training robust dialogue systems from minimal data.
arXiv Detail & Related papers (2020-12-05T02:51:09Z) - Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired
Data [61.71319905364992]
We propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data.
A data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data.
A ranking module is employed to filter out low-quality dialogues.
A model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs.
arXiv Detail & Related papers (2020-09-20T13:06:38Z) - Modelling Hierarchical Structure between Dialogue Policy and Natural
Language Generator with Option Framework for Task-oriented Dialogue System [49.39150449455407]
HDNO is an option framework for designing latent dialogue acts to avoid designing specific dialogue act representations.
We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA.
arXiv Detail & Related papers (2020-06-11T20:55:28Z) - 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)
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.