# Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

arXiv: https://arxiv.org/abs/2005.00691 · HTML: https://mlsanity.com/paper/2005.00691/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 30 of 44)

- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [GreaseLM: Graph REASoning Enhanced Language Models for Question Answering](https://mlsanity.com/paper/2201.08860/) (2022)
- [ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://mlsanity.com/paper/2303.16421/) (2023)
- [RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge](https://mlsanity.com/paper/2101.00376/) (2021)
- [PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales](https://mlsanity.com/paper/2211.01562/) (2022)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention](https://mlsanity.com/paper/2112.03254/) (2021)
- [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2201.12438/) (2022)
- [Kformer: Knowledge Injection in Transformer Feed-Forward Layers](https://mlsanity.com/paper/2201.05742/) (2022)
- [Analyzing Commonsense Emergence in Few-shot Knowledge Models](https://mlsanity.com/paper/2101.00297/) (2021)
- [Graph Reasoning for Question Answering with Triplet Retrieval](https://mlsanity.com/paper/2305.18742/) (2023)
- [Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources](https://mlsanity.com/paper/2103.11320/) (2021)
- [GNN is a Counter? Revisiting GNN for Question Answering](https://mlsanity.com/paper/2110.03192/) (2021)
- [Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering](https://mlsanity.com/paper/2105.11776/) (2021)
- [GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering](https://mlsanity.com/paper/2303.12320/) (2023)
- [Exploiting Reasoning Chains for Multi-hop Science Question Answering](https://mlsanity.com/paper/2109.02905/) (2021)
- [Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization](https://mlsanity.com/paper/2201.00136/) (2022)
- [Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference](https://mlsanity.com/paper/2306.10790/) (2023)
- [A Semantic-based Method for Unsupervised Commonsense Question Answering](https://mlsanity.com/paper/2105.14781/) (2021)
- [Do Language Models Perform Generalizable Commonsense Inference?](https://mlsanity.com/paper/2106.11533/) (2021)
- [Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer](https://mlsanity.com/paper/2203.07519/) (2022)
- [Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks](https://mlsanity.com/paper/2311.05085/) (2023)
- [Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation](https://mlsanity.com/paper/2205.09314/) (2022)
- [SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning](https://mlsanity.com/paper/2104.08793/) (2021)
- [Contextualized Scene Imagination for Generative Commonsense Reasoning](https://mlsanity.com/paper/2112.06318/) (2021)
- [Relation-Aware Language-Graph Transformer for Question Answering](https://mlsanity.com/paper/2212.00975/) (2022)
- [Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/2305.08135/) (2023)
- [Graph Language Models](https://mlsanity.com/paper/2401.07105/) (2024)
- [ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering](https://mlsanity.com/paper/2211.07065/) (2022)
- [Event Transition Planning for Open-ended Text Generation](https://mlsanity.com/paper/2204.09453/) (2022)

## Abstract (arXiv)

Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning. However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge. In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task.