ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021.emnlp-main)
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| Challenge: | Current commonsense-reasoning tasks are discriminative in nature, where a model answers a multiple-choice question for a certain context. |
| Approach: | They propose a generative task that generates a commonsense-augmented graph for stance prediction by using a create-verify-and-refine graph collection framework. |
| Outcome: | The proposed model is able to generate a graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. |
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Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths (2020.aacl-main)
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| Challenge: | Existing tasks that use commonsense reasoning as multi-choice reading comprehension lack direct assessment to machine commonsence and impede its practicability to realistic scenarios. |
| Approach: | They propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. |
| Outcome: | The proposed model outperforms the state-of-the-art models in automatic and human evaluation. |
Explanation Graph Generation via Generative Pre-training over Synthetic Graphs (2023.findings-acl)
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| Challenge: | Existing frameworks for explanation graph generation are limited due to the large number of datasets available. |
| Approach: | They propose a text-to-graph generative task to pre-train a model to bridge the text-graph gap. |
| Outcome: | The proposed framework surpasses all baseline systems with remarkable margins on ExplaGraphs and CommonsenseQA. |
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. |
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Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| 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. |
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Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
| Approach: | They propose to use pre-trained sequence-to-sequence language models to generate graphs . they propose to learn structural constraints and semantics of graphs with limited supervision . |
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GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022.coling-1)
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| Challenge: | Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. |
| Approach: | They propose a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning. |
| Outcome: | The proposed framework improves SocialIQA, CODAH, HellaSwag and CommonsenseQA . it also performs well for generative tasks like ProtoQA proving its robustness to adversaries . |
Relevant CommonSense Subgraphs for “What if...” Procedural Reasoning (2022.findings-acl)
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| Challenge: | Existing knowledge graphs and commonsense are used to learn causal reasoning over procedural text. |
| Approach: | They propose a multi-hop graph reasoning model to efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph and predict the causal answer by reasoning over the representations obtained from the commonsen subgraph and contextual interactions between the questions and context. |
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GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (L18-1)
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| Challenge: | Existing methods of automated inference do not provide enough gold explanations to train models . standardized science exams are a challenge task for question answering . |
| Approach: | They propose to manually construct a corpus of explanations for standardized science exams . they also provide an explanation-centered tablestore that contains the knowledge to construct these explanations . |
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Reframing Human-AI Collaboration for Generating Free-Text Explanations (2022.naacl-main)
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| Challenge: | Large language models are capable of generating fluent-appearing text with little task-specific supervision. |
| Approach: | They propose a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop. |
| Outcome: | The proposed model can generate freetext explanations in a fewshot setting with human-written examples. |