Structured Attention for Unsupervised Dialogue Structure Induction

• URL: http://arxiv.org/abs/2009.08552v2
• Date: Fri, 9 Oct 2020 18:33:18 GMT
• Title: Structured Attention for Unsupervised Dialogue Structure Induction
• Authors: Liang Qiu, Yizhou Zhao, Weiyan Shi, Yuan Liang, Feng Shi, Tao Yuan, Zhou Yu, Song-Chun Zhu
• Abstract summary: We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
• Score: 110.12561786644122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation.

Related papers

• Unsupervised Mutual Learning of Dialogue Discourse Parsing and Topic Segmentation [38.956438905614256]
  rhetorical structure and topic structure are mostly modeled separately or with one assisting the other in the prior work. We propose an unsupervised mutual learning framework of two structures leveraging the global and local connections between them. We also incorporate rhetorical structures into the topic structure through a graph neural network model to ensure local coherence consistency.
  arXiv  Detail & Related papers  (2024-05-30T08:10:50Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation [38.60073402817218]
  Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. We present a new framework for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models.
  arXiv  Detail & Related papers  (2023-03-02T09:27:11Z)
• Unsupervised Learning of Hierarchical Conversation Structure [50.29889385593043]
  Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks.
  arXiv  Detail & Related papers  (2022-05-24T17:52:34Z)
• 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)
• DSBERT:Unsupervised Dialogue Structure learning with BERT [4.171523157658394]
  We propose a Bert-based unsupervised dialogue structure learning algorithm DSBERT (Dialogue Structure BERT) Different from the previous SOTA models VRNN and SVRNN, we combine BERT and AutoEncoder, which can effectively combine context information. Experimental results show that DSBERT can generate a dialogue structure closer to the real structure, can distinguish sentences with different semantics and map them to different hidden states.
  arXiv  Detail & Related papers  (2021-11-09T03:31:18Z)
• Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction [10.381257436462116]
  We introduce SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE. Specifically, we learn latent structures to capture the relationships among tokens beyond the utterance boundaries. During the learning process, our speaker-specific regularization method progressively highlights speaker-related key clues and erases the irrelevant ones.
  arXiv  Detail & Related papers  (2021-09-11T04:24:51Z)
• Discovering Dialog Structure Graph for Open-Domain Dialog Generation [51.29286279366361]
  We conduct unsupervised discovery of dialog structure from chitchat corpora. We then leverage it to facilitate dialog generation in downstream systems. We present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure.
  arXiv  Detail & Related papers  (2020-12-31T10:58:37Z)
• 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.