There is a lot of research interest in encoding variable length sentences
into fixed length vectors, in a way that preserves the sentence meanings. Two
common methods include representations based on averaging word vectors, and
representations based on the hidden states of recurrent neural networks such as
LSTMs. The sentence vectors are used as features for subsequent machine
learning tasks or for pre-training in the context of deep learning. However,
not much is known about the properties that are encoded in these sentence
representations and about the language information they capture. We propose a
framework 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 score
representations by the ability to train a classifier to solve each prediction
task when using the representation as input. We demonstrate the potential
contribution of the approach by analyzing different sentence representation
mechanisms. The analysis sheds light on the relative strengths of different
sentence embedding methods with respect to these low level prediction tasks,
and on the effect of the encoded vector's dimensionality on the resulting
representations.

通过对句子结构的预测任务来评估句子嵌入向量的表现，探讨了句子嵌入方法的相对优劣和向量维度对嵌入效果的影响。