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There is a lot of research interest in encoding variable length sentencesinto fixed length vectors, in a way that preserves the sentence meanings. Twocommon methods include representations based on averaging word vectors, andrepresentations based on the hidden states of recurrent neural networks such asLSTMs. The sentence vectors are used as features for subsequent machinelearning tasks or for pre-training in the context of deep learning. However,not much is known about the properties that are encoded in these sentencerepresentations and about the language information they capture. We propose aframework that facilitates better understanding of the encoded representations.We define prediction tasks around isolated aspects of sentence structure(namely sentence length, word content, and word order), and scorerepresentations by the ability to train a classifier to solve each predictiontask when using the representation as input. We demonstrate the potentialcontribution of the approach by analyzing different sentence representationmechanisms. The analysis sheds light on the relative strengths of differentsentence embedding methods with respect to these low level prediction tasks,and on the effect of the encoded vector's dimensionality on the resultingrepresentations.

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks