Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
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.
The authors present a methodology for analyzing sentence embedding techniques by checking how much the embeddings preserve information about sentence length, word content, and word order. They examine several popular embedding methods including autoencoding LSTMs, averaged word vectors, and skip-thought vectors. The experiments are thorough and provide interesting insights into the representational power of common sentence embedding strategies, such as the fact that word ordering is surprisingly low-entropy conditioned on word content. Exploring what sort of information is encoded in representation learning methods for NLP is an important and under-researched area. For example, the tide of word-embeddings research was mostly stemmed after a thread of careful experimental results showing most embeddings to be essentially equivalent, culminating in *Improving Distributional Similarity with Lessons Learned from Word Embeddings* by Levy, Goldberg, and Dagan. As representation learning becomes even more important in NLP this sort of research will be even more important. While this paper makes a valuable contribution in setting out and exploring a methodology for evaluating sentence embeddings, the evaluations themselves are quite simple and do not necessarily correlate with real-world desiderata for sentence embeddings (as the authors note in other comments, performance on these tasks is not a normative measure of embedding quality). For example, as the authors note, the ability of the averaged vector to encode sentence length is trivially to be expected given the central limit theorem (or more accurately, concentration inequalities like Hoeffding*s inequality). The word-order experiments were interesting. A relevant citation for this sort of conditional ordering procedure is *Generating Text with Recurrent Neural Networks* by Sutskever, Martens, and Hinton, who refer to the conversion of a bag of words into a sentence as *debagging.* Although this is just a first step in better understanding of sentence embeddings, it is an important one and I recommend this paper for publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors present a methodology for analyzing sentence embedding techniques by checking how much the embeddings preserve information about sentence length, word content, and word order. They examine several popular embedding methods including autoencoding LSTMs, averaged word vectors, and skip-thought vectors. The experiments are thorough and provide interesting insights into the representational power of common sentence embedding strategies, such as the fact that word ordering is surprisingly low-entropy conditioned on word content. Exploring what sort of information is encoded in representation learning methods for NLP is an important and under-researched area. For example, the tide of word-embeddings research was mostly stemmed after a thread of careful experimental results showing most embeddings to be essentially equivalent, culminating in *Improving Distributional Similarity with Lessons Learned from Word Embeddings* by Levy, Goldberg, and Dagan. As representation learning becomes even more important in NLP this sort of research will be even more important. While this paper makes a valuable contribution in setting out and exploring a methodology for evaluating sentence embeddings, the evaluations themselves are quite simple and do not necessarily correlate with real-world desiderata for sentence embeddings (as the authors note in other comments, performance on these tasks is not a normative measure of embedding quality). For example, as the authors note, the ability of the averaged vector to encode sentence length is trivially to be expected given the central limit theorem (or more accurately, concentration inequalities like Hoeffding*s inequality). The word-order experiments were interesting. A relevant citation for this sort of conditional ordering procedure is *Generating Text with Recurrent Neural Networks* by Sutskever, Martens, and Hinton, who refer to the conversion of a bag of words into a sentence as *debagging.* Although this is just a first step in better understanding of sentence embeddings, it is an important one and I recommend this paper for publication.
assistant
The authors present a methodology for analyzing sentence embedding techniques by checking how much the embeddings preserve information about sentence length, word content, and word order. They examine several popular embedding methods including autoencoding LSTMs, averaged word vectors, and skip-thought vectors. The experiments are thorough and provide interesting insights into the representational power of common sentence embedding strategies, such as the fact that word ordering is surprisingly low-entropy conditioned on word content. Exploring what sort of information is encoded in representation learning methods for NLP is an important and under-researched area. For example, the tide of word-embeddings research was mostly stemmed after a thread of careful experimental results showing most embeddings to be essentially equivalent, culminating in *Improving Distributional Similarity with Lessons Learned from Word Embeddings* by Levy, Goldberg, and Dagan. As representation learning becomes even more important in NLP this sort of research will be even more important. While this paper makes a valuable contribution in setting out and exploring a methodology for evaluating sentence embeddings, the evaluations themselves are quite simple and do not necessarily correlate with real-world desiderata for sentence embeddings (as the authors note in other comments, performance on these tasks is not a normative measure of embedding quality). For example, as the authors note, the ability of the averaged vector to encode sentence length is trivially to be expected given the central limit theorem (or more accurately, concentration inequalities like Hoeffding*s inequality). The word-order experiments were interesting. A relevant citation for this sort of conditional ordering procedure is *Generating Text with Recurrent Neural Networks* by Sutskever, Martens, and Hinton, who refer to the conversion of a bag of words into a sentence as *debagging. * Although this is just a first step in better understanding of sentence embeddings, it is an important one and I recommend this paper for publication.