# DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge

arXiv: https://arxiv.org/abs/2101.00154 · HTML: https://mlsanity.com/paper/2101.00154/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Inductive Relation Prediction by Subgraph Reasoning](https://mlsanity.com/paper/1911.06962/) (2019)
- [Commonsense Knowledge Mining from Pretrained Models](https://mlsanity.com/paper/1909.00505/) (2019)
- [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018)
- [A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation](https://mlsanity.com/paper/2001.05139/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [Explicit Knowledge-based Reasoning for Visual Question Answering](https://mlsanity.com/paper/1511.02570/) (2015)
- [Story Ending Generation with Incremental Encoding and Commonsense Knowledge](https://mlsanity.com/paper/1808.10113/) (2018)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [Reasoning about Actions and State Changes by Injecting Commonsense Knowledge](https://mlsanity.com/paper/1808.10012/) (2018)
- [Inductive Learning on Commonsense Knowledge Graph Completion](https://mlsanity.com/paper/2009.09263/) (2020)
- [Event Representation Learning Enhanced with External Commonsense Knowledge](https://mlsanity.com/paper/1909.05190/) (2019)
- [Augmenting End-to-End Dialog Systems with Commonsense Knowledge](https://mlsanity.com/paper/1709.05453/) (2017)
- [Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension](https://mlsanity.com/paper/1803.00191/) (2018)

## Built on by (most cited first, 26 of 26)

- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature](https://mlsanity.com/paper/2405.04819/) (2024)
- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models](https://mlsanity.com/paper/2206.14268/) (2022)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset](https://mlsanity.com/paper/2109.07679/) (2021)
- [ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2305.07797/) (2023)
- [MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset](https://mlsanity.com/paper/2406.02106/) (2024)
- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association](https://mlsanity.com/paper/2505.15196/) (2025)
- [C3KG: A Chinese Commonsense Conversation Knowledge Graph](https://mlsanity.com/paper/2204.02549/) (2022)
- [MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation](https://mlsanity.com/paper/2210.07570/) (2022)
- [Intention Knowledge Graph Construction for User Intention Relation Modeling](https://mlsanity.com/paper/2412.11500/) (2024)
- [PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population](https://mlsanity.com/paper/2210.07988/) (2022)
- [Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation](https://mlsanity.com/paper/2312.05276/) (2023)
- [VCD: A Dataset for Visual Commonsense Discovery in Images](https://mlsanity.com/paper/2402.17213/) (2024)
- [Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce](https://mlsanity.com/paper/2205.10843/) (2022)
- [QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering](https://mlsanity.com/paper/2310.11303/) (2023)
- [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://mlsanity.com/paper/2310.12011/) (2023)
- [On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions](https://mlsanity.com/paper/2406.10885/) (2024)
- [Free Lunch for Efficient Textual Commonsense Integration in Language Models](https://mlsanity.com/paper/2305.15516/) (2023)
- [BEKG: A Built Environment Knowledge Graph](https://mlsanity.com/paper/2211.02864/) (2022)
- [Profiling the news spreading barriers using news headlines](https://mlsanity.com/paper/2304.11088/) (2023)

## Abstract (arXiv)

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.