Towards Universal Paraphrastic Sentence Embeddings

TL;DR

This paper evaluates various architectures for learning universal paraphrastic sentence embeddings, finding simple averaging models effective across domains and demonstrating their utility in multiple NLP tasks, with resources released for community use.

Contribution

It compares six compositional architectures for sentence embeddings, highlighting the efficiency and effectiveness of simple averaging models across diverse NLP tasks and domains.

Findings

Simple averaging models outperform LSTMs out-of-domain.

LSTMs achieve state-of-the-art on sentiment classification.

Pretrained embeddings improve performance on similarity and entailment.

Abstract

We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We compare six compositional architectures, evaluating them on annotated textual similarity datasets drawn both from the same distribution as the training data and from a wide range of other domains. We find that the most complex architectures, such as long short-term memory (LSTM) recurrent neural networks, perform best on the in-domain data. However, in out-of-domain scenarios, simple architectures such as word averaging vastly outperform LSTMs. Our simplest averaging model is even competitive with systems tuned for the particular tasks while also being extremely efficient and easy to use. In order to better understand how these architectures compare, we conduct further experiments on three supervised NLP tasks: sentence similarity, entailment, and sentiment classification. We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs. However, on sentiment classification, we find that the LSTM performs very strongly-even recording new state-of-the-art performance on the Stanford Sentiment Treebank. We then demonstrate how to combine our pretrained sentence embeddings with these supervised tasks, using them both as a prior and as a black box feature extractor. This leads to performance rivaling the state of the art on the SICK similarity and entailment tasks. We release all of our resources to the research community with the hope that they can serve as the new baseline for further work on universal sentence embeddings.