# SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference

## TL;DR

SherLIiC is a challenging new benchmark for lexical inference in context, featuring a large, manually annotated dataset with typed relations, designed to evaluate and improve natural language inference models.

## Contribution

It introduces SherLIiC, a novel, difficult benchmark dataset for lexical inference that includes typed relations and tests the limits of current NLI systems.

## Key findings

- SherLIiC is significantly more challenging than existing testbeds.
- Many correct InfCands in SherLIiC are novel and absent from current rule bases.
- State-of-the-art NLI models perform poorly on SherLIiC.

## Abstract

We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. Each InfCand consists of one of these relations, expressed as a lemmatized dependency path, and two argument placeholders, each linked to one or more Freebase types. Due to our candidate selection process based on strong distributional evidence, SherLIiC is much harder than existing testbeds because distributional evidence is of little utility in the classification of InfCands. We also show that, due to its construction, many of SherLIiC's correct InfCands are novel and missing from existing rule bases. We evaluate a number of strong baselines on SherLIiC, ranging from semantic vector space models to state of the art neural models of natural language inference (NLI). We show that SherLIiC poses a tough challenge to existing NLI systems.