# Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

arXiv: https://arxiv.org/abs/1809.05724 · HTML: https://mlsanity.com/paper/1809.05724/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2018.

## Built on by (most cited first, 30 of 48)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [Making Large Language Models Better Reasoners with Step-Aware Verifier](https://mlsanity.com/paper/2206.02336/) (2022)
- [GreaseLM: Graph REASoning Enhanced Language Models for Question Answering](https://mlsanity.com/paper/2201.08860/) (2022)
- [Cumulative Reasoning with Large Language Models](https://mlsanity.com/paper/2308.04371/) (2023)
- [MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models](https://mlsanity.com/paper/2308.09729/) (2023)
- [Graph Neural Prompting with Large Language Models](https://mlsanity.com/paper/2309.15427/) (2023)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Kformer: Knowledge Injection in Transformer Feed-Forward Layers](https://mlsanity.com/paper/2201.05742/) (2022)
- [Graph Reasoning for Question Answering with Triplet Retrieval](https://mlsanity.com/paper/2305.18742/) (2023)
- [Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14170/) (2021)
- [Biomedical knowledge graph-optimized prompt generation for large language models](https://mlsanity.com/paper/2311.17330/) (2023)
- [SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining](https://mlsanity.com/paper/2108.08983/) (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)
- [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://mlsanity.com/paper/2303.03922/) (2023)
- [Meta Prompting for AI Systems](https://mlsanity.com/paper/2311.11482/) (2023)
- [RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2109.10410/) (2021)
- [A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering](https://mlsanity.com/paper/2201.05299/) (2022)
- [ComFact: A Benchmark for Linking Contextual Commonsense Knowledge](https://mlsanity.com/paper/2210.12678/) (2022)
- [Box Embeddings: An open-source library for representation learning using geometric structures](https://mlsanity.com/paper/2109.04997/) (2021)
- [Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings](https://mlsanity.com/paper/2109.07833/) (2021)
- [A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference](https://mlsanity.com/paper/2205.15550/) (2022)
- [FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering](https://mlsanity.com/paper/2302.11799/) (2023)
- [SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning](https://mlsanity.com/paper/2104.08793/) (2021)
- [Relation-Aware Language-Graph Transformer for Question Answering](https://mlsanity.com/paper/2212.00975/) (2022)
- [Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs](https://mlsanity.com/paper/2405.17846/) (2024)
- [Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph](https://mlsanity.com/paper/2411.19064/) (2024)
- [KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph](https://mlsanity.com/paper/2103.08893/) (2021)

## Abstract (arXiv)

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention thanks to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge -- a central topic in artificial intelligence -- has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-to-graph based models, and discuss implications for the use of external knowledge in solving the NLI problem. Our model achieves the new state-of-the-art performance on the NLI problem over the SciTail science questions dataset.