Uncovering Main Causalities for Long-tailed Information Extraction

• URL: http://arxiv.org/abs/2109.05213v1
• Date: Sat, 11 Sep 2021 08:08:24 GMT
• Title: Uncovering Main Causalities for Long-tailed Information Extraction
• Authors: Guoshun Nan, Jiaqi Zeng, Rui Qiao, Zhijiang Guo and Wei Lu
• Abstract summary: Long-tailed distributions caused by the selection bias of a dataset may lead to incorrect correlations. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data.
• Score: 14.39860866665021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset, may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.

Related papers

• Towards Deconfounded Image-Text Matching with Causal Inference [36.739004282369656]
  We propose an innovative Deconfounded Causal Inference Network (DCIN) for image-text matching task. DCIN decomposes the intra- and inter-modal confounders and incorporates them into the encoding stage of visual and textual features. It can learn causality instead of spurious correlations caused by dataset bias.
  arXiv  Detail & Related papers  (2024-08-22T11:04:28Z)
• Do Finetti: On Causal Effects for Exchangeable Data [45.96632286841583]
  We study causal effect estimation in a setting where the data are not i.i.d. We focus on exchangeable data satisfying an assumption of independent causal mechanisms.
  arXiv  Detail & Related papers  (2024-05-29T07:31:18Z)
• 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)
• 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)
• Towards Causal Representation Learning and Deconfounding from Indefinite Data [17.793702165499298]
  Non-statistical data (e.g., images, text, etc.) encounters significant conflicts in terms of properties and methods with traditional causal data. We redefine causal data from two novel perspectives and then propose three data paradigms. We implement the above designs as a dynamic variational inference model, tailored to learn causal representation from indefinite data.
  arXiv  Detail & Related papers  (2023-05-04T08:20:37Z)
• Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis [56.84237932819403]
  This paper aims to estimate and mitigate the bad effect of textual modality for strong OOD generalization. Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis.
  arXiv  Detail & Related papers  (2022-07-24T03:57:40Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Link Prediction on N-ary Relational Data Based on Relatedness Evaluation [61.61555159755858]
  We propose a method called NaLP to conduct link prediction on n-ary relational data. We represent each n-ary relational fact as a set of its role and role-value pairs. Experimental results validate the effectiveness and merits of the proposed methods.
  arXiv  Detail & Related papers  (2021-04-21T09:06:54Z)

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.