Learning a Structural Causal Model for Intuition Reasoning in Conversation

• URL: http://arxiv.org/abs/2305.17727v2
• Date: Tue, 16 Jan 2024 09:07:37 GMT
• Title: Learning a Structural Causal Model for Intuition Reasoning in Conversation
• Authors: Hang Chen, Bingyu Liao, Jing Luo, Wenjing Zhu, Xinyu Yang
• Abstract summary: Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models. We develop a conversation cognitive model ( CCM) that explains how each utterance receives and activates channels of information. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds.
• Score: 20.243323155177766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component of it, remains largely unexplored due to the absence of a well-designed cognitive model. In this paper, inspired by intuition theory on conversation cognition, we develop a conversation cognitive model (CCM) that explains how each utterance receives and activates channels of information recursively. Besides, we algebraically transformed CCM into a structural causal model (SCM) under some mild assumptions, rendering it compatible with various causal discovery methods. We further propose a probabilistic implementation of the SCM for utterance-level relation reasoning. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds. Moreover, we constructed synthetic and simulated datasets incorporating implicit causes and complete cause labels, alleviating the current situation where all available datasets are implicit-causes-agnostic. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods on synthetic, simulated, and real-world datasets. Finally, we analyze the performance of CCM under latent confounders and propose theoretical ideas for addressing this currently unresolved issue.

Related papers

• Revisiting Spurious Correlation in Domain Generalization [12.745076668687748]
  We build a structural causal model (SCM) to describe the causality within data generation process. We further conduct a thorough analysis of the mechanisms underlying spurious correlation. In this regard, we propose to control confounding bias in OOD generalization by introducing a propensity score weighted estimator.
  arXiv  Detail & Related papers  (2024-06-17T13:22:00Z)
• Distribution-consistency Structural Causal Models [6.276417011421679]
  We introduce a novel textitdistribution-consistency assumption, and in alignment with it, we propose the Distribution-consistency Structural Causal Models (DiscoSCMs) To concretely reveal the enhanced model capacity, we introduce a new identifiable causal parameter, textitthe probability of consistency, which holds practical significance within DiscoSCM alone.
  arXiv  Detail & Related papers  (2024-01-29T06:46:15Z)
• Causal Optimal Transport of Abstractions [8.642152250082368]
  Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity. We propose COTA, the first method to learn abstraction maps from observational and interventional data without assuming complete knowledge of the underlying SCMs. We extensively evaluate COTA on synthetic and real world problems, and showcase its advantages over non-causal, independent and aggregated COTA formulations.
  arXiv  Detail & Related papers  (2023-12-13T12:54:34Z)
• Targeted Reduction of Causal Models [55.11778726095353]
  Causal Representation Learning offers a promising avenue to uncover interpretable causal patterns in simulations. We introduce Targeted Causal Reduction (TCR), a method for condensing complex intervenable models into a concise set of causal factors. Its ability to generate interpretable high-level explanations from complex models is demonstrated on toy and mechanical systems.
  arXiv  Detail & Related papers  (2023-11-30T15:46:22Z)
• SSL Framework for Causal Inconsistency between Structures and Representations [23.035761299444953]
  Cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text. We theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC) CCC could potentially play an influential role in various fields.
  arXiv  Detail & Related papers  (2023-10-28T08:29:49Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Inducing Causal Structure for Abstractive Text Summarization [76.1000380429553]
  We introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
  arXiv  Detail & Related papers  (2023-08-24T16:06:36Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• Case-Based Abductive Natural Language Inference [4.726777092009554]
  Case-Based Abductive Natural Language Inference (CB-ANLI) Case-Based Abductive Natural Language Inference (CB-ANLI)
  arXiv  Detail & Related papers  (2020-09-30T09:50:39Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

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.