# Natural Language Inference from Multiple Premises

arXiv: https://arxiv.org/abs/1710.02925 · HTML: https://mlsanity.com/paper/1710.02925/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 13 of 13)

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- [Exploring Continual Learning of Compositional Generalization in NLI](https://mlsanity.com/paper/2403.04400/) (2024)
- [Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases](https://mlsanity.com/paper/2112.08326/) (2021)
- [Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation](https://mlsanity.com/paper/2309.07624/) (2023)
- [From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering](https://mlsanity.com/paper/2412.17701/) (2024)
- [Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks](https://mlsanity.com/paper/2502.04797/) (2025)
- [InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding](https://mlsanity.com/paper/2509.16534/) (2025)
- [KL Regularized Normalization Framework for Low Resource Tasks](https://mlsanity.com/paper/2212.11275/) (2022)
- [Structural Chain-of-Thought: Reasoning with Executable Computational Graphs](https://mlsanity.com/paper/2610.15107/) (2026)

## Abstract (arXiv)

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.