Natural language inference (NLI) is the task of determining if a natural language hypothesis
can be inferred from a given premise in a justifiable manner. NLI was proposed as a …

Natural language inference (NLI) is a fundamentally important task in natural language
processing that has many applications. The recently released Stanford Natural Language …

This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-
2). 1 We describe the RTE-2 dataset and overview the submissions for the challenge. One of …

Inference has been a central topic in artificial intelligence from the start, but while automatic
methods for formal deduction have advanced tremendously, comparatively little progress …

We propose a model of natural language inference which identifies valid inferences by their
lexical and syntactic features, without full semantic interpretation. We extend past work in …

Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language
understanding system can reasonably be expected to handle. We present EQUATE …

The alignment problem—establishing links between corresponding phrases in two related
sentences—is as important in natural language inference (NLI) as it is in machine translation …

Many tasks in NLP stand to benefit from robust measures of semantic similarity for units
above the level of individual words. Rich semantic resources such as WordNet provide local …

Pedestrian detection and semantic segmentation are high potential tasks for many real-time
applications. However most of the top performing approaches provide state of art results at …

This paper describes a new method for recognizing whether a student's response to an
automated tutor's question entails that they understand the concepts being taught. We …