Summarization, is to reduce the size of the document while preserving the meaning, is one
of the most researched areas among the Natural Language Processing (NLP) community …

This paper focuses on paraphrase generation, which is a widely studied natural language
generation task in NLP. With the development of neural models, paraphrase generation …

We propose syntactically controlled paraphrase networks (SCPNs) and use them to
generate adversarial examples. Given a sentence and a target syntactic form (eg, a …

The lack of large-scale question matching corpora greatly limits the development of
matching methods in question answering (QA) system, especially for non-English …

A lack of standard datasets and evaluation metrics has prevented the field of paraphrasing
from making the kind of rapid progress enjoyed by the machine translation community over …

Paraphrase detection is the task of examining two sentences and determining whether they
have the same meaning. In order to obtain high accuracy on this task, thorough syntactic …

An important part of our information-gathering behavior has always been to find out what
other people think. With the growing availability and popularity of opinion-rich resources …

We introduce a stochastic graph-based method for computing relative importance of textual
units for Natural Language Processing. We test the technique on the problem of Text …

Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …

An obstacle to research in automatic paraphrase identification and generation is the lack of
large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper …