Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

Society and individuals are negatively influenced both politically and socially by the
widespread increase of fake news either way generated by humans or machines. In the era …

Semantic Textual Similarity (STS) measures the meaning similarity of sentences.
Applications include machine translation (MT), summarization, generation, question …

When experiencing an information need, users want to engage with a domain expert, but
often turn to an information retrieval system, such as a search engine, instead. Classical …

Style transfer is the task of automatically transforming a piece of text in one particular style
into another. A major barrier to progress in this field has been a lack of training and …

Motivation Sequence-based protein–protein interaction (PPI) prediction represents a
fundamental computational biology problem. To address this problem, extensive research …

How to model a pair of sentences is a critical issue in many NLP tasks such as answer
selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) …

This paper presents a computationally efficient machine-learned method for natural
language response suggestion. Feed-forward neural networks using n-gram embedding …

We consider the problem of learning general-purpose, paraphrastic sentence embeddings
based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We …