Modeling Long Context for Task-Oriented Dialogue State Generation

• URL: http://arxiv.org/abs/2004.14080v1
• Date: Wed, 29 Apr 2020 11:02:25 GMT
• Title: Modeling Long Context for Task-Oriented Dialogue State Generation
• Authors: Jun Quan and Deyi Xiong
• Abstract summary: 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.
• Score: 51.044300192906995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. By enabling the model to learn a better representation of the long dialogue context, 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.

Related papers

• Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking [57.92608483099916]
  Large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks. For more complex tasks such as dialogue state tracking (DST), designing prompts that reliably convey the desired intent is nontrivial. We introduce a saliency model to limit dialogue text length, allowing us to include more exemplars per query.
  arXiv  Detail & Related papers  (2023-02-12T15:05:10Z)
• STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension [42.57581945778631]
  Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing. We propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization. We show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
  arXiv  Detail & Related papers  (2022-12-24T04:39:54Z)
• DialogZoo: Large-Scale Dialog-Oriented Task Learning [52.18193690394549]
  We aim to build a unified foundation model which can solve massive diverse dialogue tasks. To achieve this goal, we first collect a large-scale well-labeled dialogue dataset from 73 publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-25T11:17:16Z)
• GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection [36.77204909711832]
  We propose a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems.
  arXiv  Detail & Related papers  (2021-11-29T15:24:36Z)
• Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking [3.34618986084988]
  We propose Coreference Dialogue State Tracker (CDST) that explicitly models the coreference feature. Experimental results on MultiWOZ 2.1 dataset show that the proposed model achieves the state-of-the-art joint goal accuracy of 56.47%.
  arXiv  Detail & Related papers  (2021-06-16T11:47:29Z)
• DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue [17.729711165119472]
  We introduce DialoGLUE (Dialogue Language Understanding Evaluation), a public benchmark consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks. We release several strong baseline models, demonstrating performance improvements over a vanilla BERT architecture and state-of-the-art results on 5 out of 7 tasks. Through the DialoGLUE benchmark, the baseline methods, and our evaluation scripts, we hope to facilitate progress towards the goal of developing more general task-oriented dialogue models.
  arXiv  Detail & Related papers  (2020-09-28T18:36:23Z)
• 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)
• 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)
• 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.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.