Papers with Commonsense Reasoning
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (2022.findings-emnlp)
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| Challenge: | Existing models rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. |
| Approach: | They propose a task where a model is required to learn whether a triple is salient . they propose supervised salience evaluation using a new Benchmark dataset . |
| Outcome: | The proposed task is based on a new Benchmark dataset of salience evaluation in e-commerce . it shows that saliency evaluation is hard, where models perform poorly on evaluation set . |
Commonsense about Human Senses: Labeled Data Collection Processes (D19-60)
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| Challenge: | Existing methods for recognizing mentions of human senses in text are lacking in common sense knowledge acquisition. |
| Approach: | They propose to use machine learning to acquire labeled data to extract common sense relationships pertaining to sense perception concepts. |
| Outcome: | The proposed method is effective when used with standard machine learning models on the task of sense recognition in text. |
Fingerprinting LLMs through Survey Item Factor Correlation: A Case Study on Humor Style Questionnaire (2025.emnlp-main)
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| Challenge: | Existing methods for evaluating LLMs focus on output accuracy, faithfulness, or alignment with human preferences, but these metrics do not capture fundamental differences in how models internally represent and relate psychological constructs. |
| Approach: | They propose to “fingerprint” LLMs through factor correlation patterns on standardized psychological assessments to deepen understanding of LLM's constructs representation. |
| Outcome: | The proposed method shows that LLMs represent constructs differently than humans . it also shows that no LLM recovers the constructs of the Humor Style Questionnaire . |
Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest (2026.acl-short)
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| Challenge: | a corpus of "bad" humor sentences from the Bulwer-Lytton Fiction Contest 1 is presented . standard humor detection models perform poorly on corpus, and these sentences combine features common in existing humor datasets with metaphor, metafiction and simile. |
| Approach: | They propose to analyze a corpus of "bad" humor sentences from the Bulwer-Lytton Fiction Contest . they use literary devices to synthesize contest-style sentences that imitate the form but exaggerate the effect . |
| Outcome: | The proposed corpus of sentences from the Bulwer-Lytton Fiction Contest 1 is analyzed . it shows that the sentences combine features common in existing humor datasets with metaphor, metafiction and simile. |
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (P18-1)
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
| Outcome: | The proposed task can be used to uncover implicit gender inequality in movie scripts. |
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives (N19-1)
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| Challenge: | To properly infer the intention of the narrator, one needs a certain degree of common sense and social intuition. |
| Approach: | They propose a task that uses common sense to extract pairs of questions that are appropriate candidates for the task. |
| Outcome: | The proposed method exploits commonalities in experiences people share online to extract pairs of semantically plausible advice-seeking questions that are appropriate candidates for the cloze task. |
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. |
Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
| Approach: | They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models. |
| Outcome: | The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size. |
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements (2023.emnlp-main)
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| Challenge: | Despite the advances of language models, they still produce text that contains trivial commonsense errors. |
| Approach: | They propose a general-purpose commonsense statement verification model that learns to estimate the plausibility of declarative statements based on commonsensical knowledge. |
| Outcome: | The proposed model outperforms existing models that can be repurposed for commonsense verification, even including GPT-3.5/ChatGPT/GPT-4. |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
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| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020.findings-emnlp)
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| Challenge: | Current methods for emotion recognition in conversations often face difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. |
| Approach: | They propose a framework that incorporates mental states, events, and causal relations to learn interactions between interlocutors participating in a conversation. |
| Outcome: | The proposed framework improves on four conversational benchmark datasets. |
Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes (2023.emnlp-main)
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| Challenge: | Existing models of event processing do not understand the essentiality of step events towards a goal event. |
| Approach: | They propose to deconstruct a goal event into a discrete representation of finer-grained (step) events, which are not equally important to the goal. |
| Outcome: | The proposed model can understand the essentiality of different step events towards a goal event. |
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)
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Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations. |
| Approach: | They propose a task to detect commonsense causation between two events in context . they propose 'contextualized commons sense causal reasoning' framework that uses covariates to remove confounding effects . |
| Outcome: | The proposed framework can detect commonsense causality more accurately than baselines. |
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding (2021.naacl-main)
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| Challenge: | a new method for generating metaphors is proposed to generate literal sentences . human evaluations show that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. |
| Approach: | They propose a method to automatically construct a parallel corpus by transforming literal sentences to metaphorical ones using commonsense inference and masked language modeling. |
| Outcome: | The proposed method generates metaphors better than baselines 66% of the time on average. |
CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users (2021.emnlp-main)
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| Challenge: | Humor detection is difficult due to individualistic and cultural differences in humor perception . authors propose a framework to generate perceived humor labels on Facebook posts . |
| Approach: | They propose a framework to generate perceived humor labels on Facebook posts . they use the naturally available user reactions to these posts to generate the labels . |
| Outcome: | The proposed framework generates perceived humor labels on Facebook posts with no manual annotation needed. |
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)
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| Challenge: | Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor. |
| Approach: | They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality. |
| Outcome: | The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions. |
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents (2025.acl-long)
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| Challenge: | Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to follow domain-oriented guidelines . they evaluate Lms on three critical aspects: adherence to diverse rules, robustness to rule updates . |
| Outcome: | The proposed benchmark evaluates LLMs on three critical aspects: adherence to diverse rules, robustness to rule updates, and alignment with human preferences. |
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues (2023.emnlp-main)
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| Challenge: | Understanding emotions during conversation is a fundamental aspect of human communication. |
| Approach: | They propose an approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. |
| Outcome: | The proposed approach improves ERC for code-mixed conversations by integrating commonsense with dialogue context. |
Temporal Common Sense Acquisition with Minimal Supervision (2020.acl-main)
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| Challenge: | Temporal common sense is crucial for understanding natural language, but its acquisition is challenging . human annotation on such concepts is costly and often not made explicit in text . |
| Approach: | They propose a method that exploits explicit and implicit mentions of temporal common sense to build a temporal similarity language model. |
| Outcome: | The proposed model gives better predictions of various dimensions of temporal common sense than the standard BERT. |
Do Children Texts Hold The Key To Commonsense Knowledge? (2022.emnlp-main)
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| Challenge: | Existing approaches to compiling commonsense knowledge (CSK) struggle with reporting bias, i.e., frequency in text sources is not a good proxy for relevance or truth. |
| Approach: | They propose that children's texts make fewer assumptions on the reader's knowledge and therefore spell out commonsense more explicitly. |
| Outcome: | The proposed approach can be leveraged in language-model-based commonsense knowledge extraction settings, where task-unspecific fine-tuning on small amounts of children texts yields significant improvements. |
The rJokes Dataset: a Large Scale Humor Collection (2020.lrec-1)
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| Challenge: | Humor is a complex language phenomenon that depends upon many factors, including topic, date, and recipient. |
| Approach: | They compile a large scale humor dataset from the Reddit r/Jokes subreddit. |
| Outcome: | The proposed dataset provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. |
Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs (2025.findings-emnlp)
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Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh, Reuben Narad, Timothy T. Rogers, Lalit K Jain, Robert D Nowak, Bob Mankoff, Jifan Zhang
| Challenge: | Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest. |
| Approach: | They propose to decompose humor understanding into three components and improve each by enhancing visual understanding through improved annotation and utilizing LLM-generated humor reasoning and explanations. |
| Outcome: | The proposed approach achieves 82.4% accuracy in caption ranking, significantly better than the previous 67% benchmark and matches the performance of world-renowned human experts in this domain. |
Time-aware COMET: A Commonsense Knowledge Model with Temporal Knowledge (2024.lrec-main)
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| Challenge: | Existing commonsense knowledge models do not consider granularity or time axes, and can't handle commonsensical knowledge, which is tacit. |
| Approach: | They propose to use ChatGPT to create a time-aware commonsense knowledge model, TaCOMET, and use it to continually fine tune existing models. |
| Outcome: | The proposed model outperforms existing models on a robotic decision-making task when proper times are input. |