---
title: "Neural Natural Language Inference Models Enhanced with"
description: "Their model, KIM, builds on the ESIM architecture and adds lexical-level external… 88.6% accuracy. 53 references, cited by 61 papers here."
authors: ["Qian Chen", "Xiaodan Zhu", "Zhen-Hua Ling", "Diana Inkpen", "Si Wei"]
year: 2018
venue: "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P18-1224/"
doi: "10.18653/v1/p18-1224"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2018-neural-natural-language-inference-models/"
---
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# Neural Natural Language Inference Models Enhanced with External Knowledge

**Authors:** Qian Chen, [Xiaodan Zhu](https://md.research.elamur.ai/free/en/authors/xiaodan-zhu/), [Zhen-Hua Ling](https://md.research.elamur.ai/free/en/authors/zhen-hua-ling/), [Diana Inkpen](https://md.research.elamur.ai/free/en/authors/diana-inkpen/) and Si Wei · **Year:** 2018 · **Venue:** [Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2018](https://md.research.elamur.ai/free/en/venues/acl/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P18-1224/) · [PDF](https://aclanthology.org/P18-1224.pdf) · [DOI 10.18653/v1/p18-1224](https://doi.org/10.18653/v1/p18-1224) · [OpenAlex](https://openalex.org/W2798416089)

**Citations:** 253 citations (OpenAlex, 2026-10-14) · [61](https://md.research.elamur.ai/free/en/papers/chen-2018-neural-natural-language-inference-models/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Neural NLI systems are usually trained end to end, with the implicit assumption that annotated corpora such as SNLI and MultiNLI contain all the inference knowledge a model needs. Chen et al. question this assumption. Their model, KIM, builds on the ESIM architecture and adds lexical-level external knowledge. For each premise–hypothesis word pair, a relation vector encodes links such as synonymy, antonymy, hypernymy, hyponymy and co-hyponymy. This vector enters the model at three points. It raises co-attention scores for related word pairs, it is attached to the local inference features, and it informs the final inference composition step. KIM reaches state-of-the-art accuracy on SNLI and MultiNLI. The gains are largest when training data is scarce and grow as more of the knowledge resource is used. KIM also proves much more robust on the lexically challenging test set of Glockner et al. (2018). The work matters because it shows that structured human knowledge can complement large-scale supervised learning.

## Key points
- On the SNLI test set, KIM scores 88.6% accuracy, compared with 87.9% for ESIM.
- Evaluated on the benchmark from Glockner et al. (2018), KIM attains 83.5%, only 5.1 points below its SNLI score. Baseline models lose 22.3–32.8 points there (ESIM: 65.6%).
- KIM beats ESIM in 13 of the 14 replacement-word categories of the Glockner set. The exception is synonyms (92.1% vs. 99.7%).
- On the annotated MultiNLI development subset, KIM edges out ESIM both in-domain (77.9 vs. 77.1) and cross-domain (77.4 vs. 76.7). In the cross-domain setting, it matches or exceeds ESIM in 10 of 13 categories, including +5.0 on antonyms.
- Ablations vary training data from 0.8% to 100%. Adding knowledge to local inference collection helps at every data size. With only 0.8% of the training data, using more than half of the relation set yields large accuracy gains.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen and Si Wei. 2018. Neural Natural Language Inference Models Enhanced with External Knowledge. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chen2018neural,
  title = {{Neural Natural Language Inference Models Enhanced with External Knowledge}},
  author = {Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen and Si Wei},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1224},
  url = {https://doi.org/10.18653/v1/p18-1224}
}
```

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*Updated 24 October 2026*