Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP
tasks to capture the long-term and local dependencies, respectively. Attention mechanisms …

We describe an approach for unsupervised learning of a generic, distributed sentence
encoder. Using the continuity of text from books, we train an encoder-decoder model that …

Because of their superior ability to preserve sequence information over time, Long Short-
Term Memory (LSTM) networks, a type of recurrent neural network with a more complex …

This paper presents the task on the evaluation of Compositional Distributional Semantics
Models on full sentences organized for the first time within SemEval-2014. Participation was …

We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for
labeled data comprised of pairs of variable-length sequences. Our model is applied to …

Word embedding, where semantic and syntactic features are captured from unlabeled text
data, is a basic procedure in Natural Language Processing (NLP). The extracted features …

Modeling sentence similarity is complicated by the ambiguity and variability of linguistic
expression. To cope with these challenges, we propose a model for comparing sentences …

We present a new release of the Paraphrase Database. PPDB 2.0 includes a
discriminatively re-ranked set of paraphrases that achieve a higher correlation with human …

Textual similarity measurement is a challenging problem, as it requires understanding the
semantics of input sentences. Most previous neural network models use coarse-grained …

Abstract The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates …