In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

We present a large-scale collection of diverse natural language inference (NLI) datasets that
help provide insight into how well a sentence representation captures distinct types of …

Extensive knowledge bases of entailment rules between predicates are crucial for applied
semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints …

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use
crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision …

In relation extraction with distant supervision, noise labels are a bottleneck problem that
hinders the performance of training models. Existing neural models solved this problem …

We use dependency triples automatically extracted from a Web-scale corpus to perform
unsupervised semantic frame induction. We cast the frame induction problem as a …

A key challenge at the core of many Natural Language Processing (NLP) tasks is the ability
to determine which conclusions can be inferred from a given natural language text. This …

Entailment rules between predicates are fundamental to many semantic-inference
applications. Consequently, learning such rules has been an active field of research in …

Learning entailment rules is fundamental in many semantic-inference applications and has
been an active field of research in recent years. In this paper we address the problem of …

Identifying entailment relations between predicates is an important part of applied semantic
inference. In this article we propose a global inference algorithm that learns such entailment …