DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge

• URL: http://arxiv.org/abs/2101.00154v2
• Date: Thu, 18 Feb 2021 12:43:37 GMT
• Title: DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge
• Authors: Tianqing Fang, Hongming Zhang, Weiqi Wang, Yangqiu Song, Bin He
• Abstract summary: We propose an alternative commonsense knowledge acquisition framework DISCOS. DISCOS populates expensive commonsense knowledge to more affordable linguistic knowledge resources. We can acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC on the core part of ASER.
• Score: 42.08569149041291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Commonsense knowledge is crucial for artificial intelligence systems to understand natural language. Previous commonsense knowledge acquisition approaches typically rely on human annotations (for example, ATOMIC) or text generation models (for example, COMET.) Human annotation could provide high-quality commonsense knowledge, yet its high cost often results in relatively small scale and low coverage. On the other hand, generation models have the potential to automatically generate more knowledge. Nonetheless, machine learning models often fit the training data well and thus struggle to generate high-quality novel knowledge. To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge to more affordable linguistic knowledge resources. Experiments demonstrate that we can successfully convert discourse knowledge about eventualities from ASER, a large-scale discourse knowledge graph, into if-then commonsense knowledge defined in ATOMIC without any additional annotation effort. Further study suggests that DISCOS significantly outperforms previous supervised approaches in terms of novelty and diversity with comparable quality. In total, we can acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC on the core part of ASER. Codes and data are available at https://github.com/HKUST-KnowComp/DISCOS-commonsense.

Related papers

• CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning [45.62134354858683]
  CANDLE is a framework that iteratively performs conceptualization and instantiation over commonsense knowledge bases. By applying CANDLE to ATOMIC, we construct a comprehensive knowledge base comprising six million conceptualizations and instantiated commonsense knowledge triples.
  arXiv  Detail & Related papers  (2024-01-14T13:24:30Z)
• Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators [78.63553017938911]
  Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks. However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
  arXiv  Detail & Related papers  (2023-10-11T08:22:37Z)
• Commonsense Knowledge Transfer for Pre-trained Language Models [83.01121484432801]
  We introduce commonsense knowledge transfer, a framework to transfer the commonsense knowledge stored in a neural commonsense knowledge model to a general-purpose pre-trained language model. It first exploits general texts to form queries for extracting commonsense knowledge from the neural commonsense knowledge model. It then refines the language model with two self-supervised objectives: commonsense mask infilling and commonsense relation prediction.
  arXiv  Detail & Related papers  (2023-06-04T15:44:51Z)
• Generated Knowledge Prompting for Commonsense Reasoning [53.88983683513114]
  We propose generating knowledge statements directly from a language model with a generic prompt format. This approach improves performance of both off-the-shelf and finetuned language models on four commonsense reasoning tasks. Notably, we find that a model's predictions can improve when using its own generated knowledge.
  arXiv  Detail & Related papers  (2021-10-15T21:58:03Z)
• Understanding Few-Shot Commonsense Knowledge Models [39.31365020474205]
  We investigate training commonsense knowledge models in a few-shot setting. We find that human quality ratings for knowledge produced from a few-shot trained system can achieve performance within 6% of knowledge produced from fully supervised systems.
  arXiv  Detail & Related papers  (2021-01-01T19:01:09Z)
• Towards a Universal Continuous Knowledge Base [49.95342223987143]
  We propose a method for building a continuous knowledge base that can store knowledge imported from multiple neural networks. Experiments on text classification show promising results. We import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model.
  arXiv  Detail & Related papers  (2020-12-25T12:27:44Z)
• TransOMCS: From Linguistic Graphs to Commonsense Knowledge [109.36596335148091]
  Conventional methods of acquiring commonsense knowledge require laborious and costly human annotations. We explore a practical way of mining commonsense knowledge from linguistic graphs, with the goal of transferring cheap knowledge obtained with linguistic patterns into expensive commonsense knowledge. Experimental results demonstrate the transferability of linguistic knowledge to commonsense knowledge and the effectiveness of the proposed approach in terms of quantity, novelty, and quality.
  arXiv  Detail & Related papers  (2020-05-01T04:03:58Z)

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.