Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data

• URL: http://arxiv.org/abs/2009.09427v2
• Date: Tue, 10 Nov 2020 08:50:49 GMT
• Title: Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
• Authors: Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao, Xiaoxi Mao, Yadong Xi, Minlie Huang
• Abstract summary: 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.
• Score: 61.71319905364992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually time-consuming and labor-intensive. To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data. Specifically, 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. Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data. Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines.

Related papers

• Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues [34.78482218571574]
  We propose a data augmentation method to automatically augment high-quality responses with different semantics by counterfactual inference. Experimental results show that our data augmentation method can augment high-quality responses with different semantics for a given dialogue history, and can outperform competitive baselines on multiple downstream tasks.
  arXiv  Detail & Related papers  (2022-10-30T13:26:49Z)
• Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding [103.94325597273316]
  We present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. For DailyDialog specifically, using 10% of the ground truth data we outperform the current state-of-the-art model which uses 100% of the data.
  arXiv  Detail & Related papers  (2022-10-25T17:01:30Z)
• 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)
• Quick Starting Dialog Systems with Paraphrase Generation [0.0]
  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.
  arXiv  Detail & Related papers  (2022-04-06T02:35:59Z)
• DG2: Data Augmentation Through Document Grounded Dialogue Generation [41.81030088619399]
  We propose an automatic data augmentation technique grounded on documents through a generative dialogue model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods.
  arXiv  Detail & Related papers  (2021-12-15T18:50:14Z)
• 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)
• Ranking Enhanced Dialogue Generation [77.8321855074999]
  How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures to model the history. This paper proposes a Ranking Enhanced Dialogue generation framework.
  arXiv  Detail & Related papers  (2020-08-13T01:49:56Z)
• Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
  This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
  arXiv  Detail & Related papers  (2020-05-27T02:05:33Z)
• Learning an Unreferenced Metric for Online Dialogue Evaluation [53.38078951628143]
  We propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.
  arXiv  Detail & Related papers  (2020-05-01T20:01:39Z)

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.