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 …

Understanding open-domain text is one of the primary challenges in NLP. Machine
comprehension evaluates the system's ability to understand text through a series of question …

We introduce two Bayesian models for unsupervised semantic role labeling (SRL) task. The
models treat SRL as clustering of syntactic signatures of arguments with clusters …

Abstract Semantic Role Labeling (SRL) has become one of the standard tasks of natural
language processing and proven useful as a source of information for a number of other …

Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be
inferred from the given premise image, which is one special task among recent emerged …

In this work, we report large-scale semantic role annotation of arguments in the Turkish
dependency treebank, and present the first comprehensive Turkish semantic role labeling …

Determining the textual entailment between texts is important in many NLP tasks, such as
summarization, question answering, and information extraction and retrieval. Various …

We introduce a new approach to unsupervised estimation of feature-rich semantic role
labeling models. Our model consists of two components:(1) an encoding component: a …

There is a rise in interest in the evaluation of meaning in real-life applications, eg, for
assessing the content of short answers. The approaches typically use a combination of …

This chapter provides an overview of applied research into recognizing textual entailment. It
identifies the fundamental challenges encountered so far, and surveys the models used to …