# Natural Language Inference over Interaction Space

arXiv: https://arxiv.org/abs/1709.04348 · HTML: https://mlsanity.com/paper/1709.04348/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 30 of 34)

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- [Pair-Level Supervised Contrastive Learning for Natural Language Inference](https://mlsanity.com/paper/2201.10927/) (2022)
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- [A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference](https://mlsanity.com/paper/2205.15550/) (2022)
- [INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations](https://mlsanity.com/paper/2209.01061/) (2022)
- [AlignVE: Visual Entailment Recognition Based on Alignment Relations](https://mlsanity.com/paper/2211.08736/) (2022)
- [Context-Aware Interaction Network for Question Matching](https://mlsanity.com/paper/2104.08451/) (2021)
- [No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference](https://mlsanity.com/paper/2306.09918/) (2023)
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- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
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- [When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews](https://mlsanity.com/paper/2310.18685/) (2023)
- [An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention](https://mlsanity.com/paper/2109.08360/) (2021)
- [Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification](https://mlsanity.com/paper/2109.01560/) (2021)
- [Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences](https://mlsanity.com/paper/2204.04888/) (2022)
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- [How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG](https://mlsanity.com/paper/2303.16757/) (2023)

## Abstract (arXiv)

Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information. One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus. It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.