Papers with ConceptNet
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
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| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
| Outcome: | The proposed model achieves 73.3 % accuracy on the official test data. |
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (2021.eacl-demos)
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| Challenge: | ConceptNet is a semantic network which contains general commonsense facts about the world, e.g., Birds can fly or Computers are used for sending e-mails. |
| Approach: | They propose a tool for Extracting Concepts from texts and linking them to ConceptNet, using the maximum relational information stored in ConceptNet. |
| Outcome: | The proposed method extracts meaningful concepts from natural language texts and links them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the KnowledgeGraph. |
Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency (2022.coling-1)
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| Challenge: | Existing evaluations of word/concept representations on verbal fluency tasks rely on human annotations of clusters and switches between sub-categories. |
| Approach: | They analyze word/concept representations in an experimental verbal fluency dataset . they find that ConceptNet embeddings outperforms other semantic representations . |
| Outcome: | The proposed method outperforms other semantic representations by a large margin. |
Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)
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| Challenge: | Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data. |
| Approach: | They evaluate adapter-based methods for adapting mLMs to low-resource languages . they use unstructured text and structured knowledge from ConceptNet to evaluate adapters . |
| Outcome: | The proposed methods outperform large language models and LLaMA-3 and deepSeek-R1 models on low training data. |
Event Semantic Knowledge in Procedural Text Understanding (2023.starsem-1)
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| Challenge: | Annotators’ reliance on commonsense knowledge to annotate implicit state information is a challenge for entity state tracking. |
| Approach: | They propose a method for entity state tracking that incorporates commonsense entity-centric knowledge from ConceptNet into a BERT-based neural-symbolic architecture. |
| Outcome: | The proposed model outperforms existing models on the ProPara dataset and is domain-agnostic. |
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)
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Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Karën Fort, Katerina Zdravkova, Iztok Kosem, Jaka Čibej, Špela Arhar Holdt, Alice Millour, Alexander König, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
| Challenge: | Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones. |
| Approach: | They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises . |
| Outcome: | The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs. |
Knowledge Graph Compression Enhances Diverse Commonsense Generation (2023.emnlp-main)
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| Challenge: | Existing models use commonsense knowledge graphs to extract subgraphs of relevant knowledge pertaining to concepts in the input but due to the large coverage and vast scale of ConceptNet, the extracted subgraph may contain loosely related, redundant and irrelevant information. |
| Approach: | They propose to apply a differentiable graph compression algorithm to extract subgraphs of relevant knowledge from input sentences. |
| Outcome: | The proposed algorithm achieves better quality-diversity tradeoff than a large language model with 100 times the number of parameters. |
Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet (2020.lrec-1)
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Christos Rodosthenous, Verena Lyding, Federico Sangati, Alexander König, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei
| Challenge: | Language resources (LRs) are expensive to create and maintain, and this makes it difficult to create or extend LRs. |
| Approach: | They propose to use a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet with new words. |
| Outcome: | The proposed model allows to gather 12,000 answers from learners on different question types over 16 days and shows that it is a potential tool for crowdsourcing and fostering vocabulary skills. |
ConceptBert: Concept-Aware Representation for Visual Question Answering (2020.findings-emnlp)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. |
| Approach: | They propose an algorithm which learns a joint Concept-Vision-Language embedding for questions which require common sense knowledge from external structured content. |
| Outcome: | The proposed model is based on the Outer Knowledge-VQA and VQA datasets. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations (2021.acl-short)
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Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
| Challenge: | Text-based games (TBGs) are useful benchmarks for evaluating progress in grounded language understanding and reinforcement learning (RL). |
| Approach: | They propose an agent that induces a graph representation of the game state and jointly grounds it with a commonsense knowledge from ConceptNet. |
| Outcome: | The proposed agent outperforms baseline agents in the proposed game . |
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference (2022.findings-acl)
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| Challenge: | Existing approaches to commonsense inference lack coverage and expressive diversity of commonsensense knowledge graphs. |
| Approach: | They propose a framework that contrasts sets of semantically similar and dissimilar events . they propose 'solar' framework that can be used to learn commonsense inference . |
| Outcome: | The proposed framework outperforms the state-of-the-art commonsense transformer on commonsensense inference by 1.84% on average among 8 metrics. |
Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning (2023.findings-acl)
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Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, Zhao Cao
| Challenge: | SylloBase is a benchmark for syllogistic reasoning, a critical capability widely required in natural language understanding tasks, such as text entailment and question answering. |
| Approach: | They propose to use a benchmark to learn syllogistic reasoning on a set of templates and to use them to generate and understand slogisms. |
| Outcome: | The proposed benchmark covers a complete taxonomy of syllogism reasoning patterns, and contains both automatically and manually constructed samples. |
Towards Knowledge-Augmented Visual Question Answering (2020.coling-main)
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| Challenge: | Visual Question Answering (VQA) is a challenging task for humans, but it is effortless for visual-based approaches. |
| Approach: | They propose a visual-based approach that captures interactions between visual scenes and external knowledge sources and exploits ConceptNet as the source of general knowledge. |
| Outcome: | The proposed model learns a question-adaptive graph representation of related knowledge instances. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models (2024.emnlp-main)
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| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
| Approach: | They propose to leverage external resources to identify semantic paths linking ADUs . they propose to use WordNet, ConceptNet, and Wikipedia to identify these paths . |
| Outcome: | The proposed architecture achieves F-scores of 0.85, 0.84, 0.70, and 0.87 on four datasets. |
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources (2020.coling-main)
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| Challenge: | Existing methods to facilitate natural language understanding rarely involve commonsense or background knowledge. |
| Approach: | They propose a question-answering method that integrates multiple knowledge sources to boost performance. |
| Outcome: | The proposed method outperforms other competing methods on the CommonsenseQA dataset and achieves the new state-of-the-art. |
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction (2020.lrec-1)
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| Challenge: | Existing systems for question generation based on shallow linguistic information at text level generate questions for reading comprehension. |
| Approach: | They propose a relation extraction system that extracts pedagogically motivated relation types from a text to generate questions for reading comprehension. |
| Outcome: | The proposed system extracts pedagogically motivated relations from a text . it performs better on the subset of relations suitable for pedagical purposes. |
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)
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| Challenge: | empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence . |
| Approach: | They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions. |
| Outcome: | The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism. |
Neuro-Symbolic Reinforcement Learning with First-Order Logic (2021.emnlp-main)
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Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
| Challenge: | Existing deep reinforcement learning methods require many trials before convergence and no direct interpretability of trained policies is provided. |
| Approach: | They propose a novel RL method which can learn symbolic and interpretable rules in their differentiable network. |
| Outcome: | The proposed method can learn symbolic and interpretable rules in their differentiable network. |
Multiple Knowledge GraphDB (MKGDB) (2020.lrec-1)
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| Challenge: | ConceptNet, DBpedia, WebIsAGraph, WordNet and Wikipedia category hierarchy are used to create a large-scale graph database. |
| Approach: | They propose to use multiple taxonomy backbones extracted from 5 existing knowledge graphs to create a large-scale graph database. |
| Outcome: | The proposed database is intended to favour and support the development of open-domain natural language processing applications relying on knowledge bases. |
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
| Challenge: | Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images. |
| Approach: | They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs. |
| Outcome: | The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks. |
VCD: A Dataset for Visual Commonsense Discovery in Images (2025.findings-acl)
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| Challenge: | Visual commonsense data sets lack visual grounded representations of commonsensense . existing knowledge bases lack visual-based knowledge tied to actual visual scenes . |
| Approach: | They present a large-scale visual commonsense dataset with over 100,000 images and 14 million object-commonsense pairs that integrates both Seen (directly observable) and Unseen (inferrable) commonsens. |
| Outcome: | The proposed model integrates Seen (directly observable) and Unseen (inferrable) commonsense across Property, Action, and Space aspects. |
Solving Hard Analogy Questions with Relation Embedding Chains (2023.emnlp-main)
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| Challenge: | Existing approaches to model how concepts are related are incomplete and noisy. |
| Approach: | They propose to model relations as paths but associate their edges with relation embeddings. |
| Outcome: | The proposed representations are useful for solving hard analogy questions. |
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021.emnlp-main)
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| Challenge: | Commonsense knowledge bases are mostly human-generated and reflect societal biases . a filtering-based approach can reduce the issues in both resources and models but leads to a performance drop . |
| Approach: | They propose a filtering-based approach to mitigating representational harms in ConceptNet and GenericsKB . they propose filtered-based approaches can reduce issues in both resources and models but leads to performance drop . |
| Outcome: | The proposed approach reduces issues in resources and models but leads to performance drop . the paper proposes a filtering-based approach that reduces biases but leaves room for future work . |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
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| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (N19-1)
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| Challenge: | Recent work on question answering relies on factoid questions with little general knowledge. |
| Approach: | They propose a dataset to capture commonsense question answering with prior knowledge . they extract multiple-choice questions that discriminate between the source and target concepts . |
| Outcome: | The proposed dataset captures commonsense reasoning beyond associations . it obtains 56% accuracy, well below human performance, which is 89% . |
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples (2022.lrec-1)
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| Challenge: | a commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. |
| Approach: | They propose a machine learning-based approach to detect semantic gaps in a commonsense knowledge graph . they use a conceptNet dataset to test the validity of two adjacent triples . |
| Outcome: | The proposed approach detects a semantic gap in a commonsense knowledge graph . the proposed approach also provides insights into the effectiveness of sense embeddings . |
COPA-SSE: Semi-structured Explanations for Commonsense Reasoning (2022.lrec-1)
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| Challenge: | Semi-structured explanations for Choice of Plausible Alternatives (COPA-SSE) are a crowdsourced dataset of 9,747 common sense explanations . |
| Approach: | They propose a semi-structured approach to explain Choice of Plausible Alternatives questions using a crowdsourced dataset of 9,747 common sense explanations with ConceptNet relations but freely written concepts. |
| Outcome: | The proposed explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for improving such systems. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach (2020.emnlp-main)
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| Challenge: | Existing studies on aspect-based abstractive summarization assume a small set of aspects and do not consider other diverse aspects. |
| Approach: | They propose a weak supervision construction method and an aspect modeling scheme to solve this problem. |
| Outcome: | The proposed method significantly expands the application of the task in practice. |
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering (2021.emnlp-main)
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| Challenge: | Existing knowledge-based visual question answering systems rely on Concept-Net and Wikipedia to obtain external knowledge. |
| Approach: | They propose a visual retriever-reader pipeline that uses a natural language knowledge base and a Visual retriever to retrieve relevant knowledge. |
| Outcome: | The proposed method significantly improves the visual retriever-reader pipeline on the OK-VQA benchmark. |
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network (D19-1)
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| Challenge: | Recent large-scale datasets specify that external knowledge is required to answer questions. |
| Approach: | They propose a model that leverages external knowledge to construct sub-graphs for entities in machine comprehension context. |
| Outcome: | The proposed model achieves state-of-the-art performance on the ReCoRD dataset. |
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation (2023.acl-long)
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| Challenge: | Existing studies can only identify sarcastic post but could not give concrete explanation for why it is sarkastic. |
| Approach: | They propose a multimodal sarcasm explanation scheme that generates a sentence for a social post to explain why it contains sarkasis. |
| Outcome: | The proposed model outperforms existing methods on a public dataset. |
Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (2025.coling-main)
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| Challenge: | Knowledge bases (KBs) in low-resource languages are often incomplete, restricting the ability to do zero-shot question answering using multilingual language models. |
| Approach: | They propose a novel cross-lingual mapping technique which improves word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment. |
| Outcome: | The proposed approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access. |
GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models excel at factual recall, arithmetic reasoning, multi-turn dialogue . their capacity as askers, formulating strategic, adaptive, and information-seeking questions, remains less explored . |
| Approach: | They propose a protocol for evaluating large language models as strategic question-askers . they propose entropy-based methods that filter candidates via ConceptNet and Bayesian method that tracks belief updates over semantic concepts . |
| Outcome: | The proposed method is model-agnostic and supports post hoc analysis. |
Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)
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| Challenge: | Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts . |
| Approach: | They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base. |
| Outcome: | The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |