Bi-directional Cognitive Thinking Network for Machine Reading Comprehension

• URL: http://arxiv.org/abs/2010.10286v1
• Date: Tue, 20 Oct 2020 13:56:30 GMT
• Title: Bi-directional Cognitive Thinking Network for Machine Reading Comprehension
• Authors: Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Jing Yu, Yajing Sun, Xiangpeng Wei
• Abstract summary: We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking.
• Score: 18.690332722963568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.

Related papers

• CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks [39.43278448546028]
  Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in various cognitive tasks. This study introduces the textbfCogniDual Framework for LLMs (CFLLMs), designed to assess whether LLMs can, through self-training, evolve from deliberate deduction to intuitive responses.
  arXiv  Detail & Related papers  (2024-09-05T09:33:24Z)
• The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms [3.3653074379567096]
  mechanistic interpretability (MI) has emerged as a distinct research area studying the features and implicit algorithms learned by foundation models such as large language models. We argue that current methods are ripe to facilitate a transition in deep learning interpretation echoing the "cognitive revolution" in 20th-century psychology. We propose a taxonomy mirroring key parallels in computational neuroscience to describe two broad categories of MI research.
  arXiv  Detail & Related papers  (2024-08-11T20:50:16Z)
• Metacognitive AI: Framework and the Case for a Neurosymbolic Approach [5.5441283041944]
  We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.
  arXiv  Detail & Related papers  (2024-06-17T23:30:46Z)
• Crystal: Introspective Reasoners Reinforced with Self-Feedback [118.53428015478957]
  We propose a novel method to develop an introspective commonsense reasoner, Crystal. To tackle commonsense problems, it first introspects for knowledge statements related to the given question, and subsequently makes an informed prediction that is grounded in the previously introspected knowledge. Experiments show that Crystal significantly outperforms both the standard supervised finetuning and chain-of-thought distilled methods, and enhances the transparency of the commonsense reasoning process.
  arXiv  Detail & Related papers  (2023-10-07T21:23:58Z)
• CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm [120.98789964518562]
  We argue that due to the large scale of commonsense knowledge, it is infeasible to annotate a large enough training set for each task to cover all commonsense for learning. We focus on investigating models' commonsense inference capabilities from two perspectives. We name the benchmark as Commonsense Inference with Knowledge-in-the-loop Question Answering (CIKQA)
  arXiv  Detail & Related papers  (2022-10-12T14:32:39Z)
• Anti-Retroactive Interference for Lifelong Learning [65.50683752919089]
  We design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain. It tackles the problem from two aspects: extracting knowledge and memorizing knowledge. It is theoretically analyzed that the proposed learning paradigm can make the models of different tasks converge to the same optimum.
  arXiv  Detail & Related papers  (2022-08-27T09:27:36Z)
• elBERto: Self-supervised Commonsense Learning for Question Answering [131.51059870970616]
  We propose a Self-supervised Bidirectional Representation Learning of Commonsense framework, which is compatible with off-the-shelf QA model architectures. The framework comprises five self-supervised tasks to force the model to fully exploit the additional training signals from contexts containing rich commonsense. elBERto achieves substantial improvements on out-of-paragraph and no-effect questions where simple lexical similarity comparison does not help.
  arXiv  Detail & Related papers  (2022-03-17T16:23:45Z)
• DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations [0.0]
  We propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking.
  arXiv  Detail & Related papers  (2021-06-03T16:47:38Z)
• Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge [77.77684299758494]
  We extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG) First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. To handle the diversity and complexity of KG information, we propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via
  arXiv  Detail & Related papers  (2021-04-12T05:52:20Z)
• Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development [1.7778609937758327]
  This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches. The proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.
  arXiv  Detail & Related papers  (2021-02-01T00:29:01Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)

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.