| Challenge: | a recent study examines the commonsense reasoning used by humans to justify an AI prediction. |
| Approach: | They propose an approach that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. |
| Outcome: | The proposed model achieves significantly higher performance in both action prediction and justification. |
Similar Papers
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (D19-1)
Copied to clipboard
| Challenge: | Understanding event and event-centered commonsense reasoning is crucial for natural language processing (NLP). |
| Approach: | They propose a If-Then commonsense reasoning dataset Atomic and an RNN-based Seq2Seq model to facilitate this. |
| Outcome: | The proposed model improves the accuracy and diversity of inferences compared with baseline methods. |
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (P18-1)
Copied to clipboard
| 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. |
Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
Copied to clipboard
| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
| Outcome: | Empirical results show that the proposed framework improves on the commonsenseQA task by 10%. |
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge (D18-1)
Copied to clipboard
| Challenge: | Recent work has shown impressive progress in comprehending procedural text, but their predictions can be inconsistent or highly improbable. |
| Approach: | They propose to incorporate global constraints and bias reading with corpora-based preferences to improve the predicted effects of actions in a paragraph. |
| Outcome: | The proposed model significantly outperforms earlier models on a benchmark dataset for procedural text comprehension (+8% relative gain) it avoids nonsensical predictions that earlier models make, and it is more robust than previous models. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Learning the Effects of Physical Actions in a Multi-modal Environment (2023.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are trained on large corpora of disembodied texts. |
| Approach: | They propose a multi-modal task of predicting the outcomes of actions solely from realistic sensory inputs (images and text). They extend an LLM to model latent representations of objects to better predict action outcomes in an environment. |
| Outcome: | The proposed model can capture commonsense when augmented with visual information and generalize and learn commonsensical reasoning better. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
Copied to clipboard
| 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. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
Copied to clipboard
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
Copied to clipboard
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |