Learning sentence embeddings using Recursive Networks

Anson Bastos

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arXiv · 1805.08353

Learning sentence embeddings using Recursive Networks

Anson Bastos

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Abstract

Learning sentence vectors that generalise well is a challenging task. In this paper we compare three methods of learning phrase embeddings: 1) Using LSTMs, 2) using recursive nets, 3) A variant of the method 2 using the POS information of the phrase. We train our models on dictionary definitions of words to obtain a reverse dictionary application similar to Felix et al. [1]. To see if our embeddings can be transferred to a new task we also train and test on the rotten tomatoes dataset [2]. We train keeping the sentence embeddings fixed as well as with fine tuning.

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Anson Bastos. 2018-05-22. Learning sentence embeddings using Recursive Networks. https://arxiv.org/abs/1805.08353

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