A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge [PDF]
, 2023 The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and empathetic dialogue systems. Due to the implicit nature of conveying emotions in many scenarios, commonsense knowledge is widely utilized to enrich utterance semantics and ...
arxiv +1 more source
Logical Reasoning over Natural Language as Knowledge Representation: A Survey [PDF]
arXiv, 2023 Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses formal language as knowledge representation and symbolic reasoners. However, reasoning with formal language has proved challenging (e.g., brittleness and knowledge-acquisition ...
arxiv
ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference [PDF]
arXiv, 2021 Neural language representation models such as BERT, pre-trained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Language Inference (NLI) is a challenging reasoning task that relies on common human understanding of ...
arxiv
Annotating Implicit Reasoning in Arguments with Causal Links [PDF]
arXiv, 2021 Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge. However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise).
arxiv
Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey [PDF]
arXiv, 2022 While commonsense knowledge acquisition and reasoning has traditionally been a core research topic in the knowledge representation and reasoning community, recent years have seen a surge of interest in the natural language processing community in developing pre-trained models and testing their ability to address a variety of newly designed commonsense ...
arxiv
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs [PDF]
arXiv, 2020 Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques.
arxiv
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering [PDF]
arXiv, 2022 Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e.g., the context in question and answer; "QA context") and structured (e.g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge.
arxiv
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA [PDF]
arXiv, 2020 One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time.
arxiv
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering [PDF]
arXiv, 2022 Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasoning. While knowledge graphs (KG) are
arxiv
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension [PDF]
arXiv, 2021 Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines. Previous methods tackle this problem by enriching word representations via pre-trained Knowledge Graph Embeddings (KGE).
arxiv