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.
This paper analyzes various unsupervised sentence embedding approaches by means of a set of auxiliary prediction tasks. By examining how well classifiers can predict word order, word content, and sentence length, the authors aim to assess how much and what type of information is captured by the different embedding models. The main focus is on a comparison between and encoder-decoder model (ED) and a permutation-invariant model, CBOW. (There is also an analysis of skip-thought vectors, but since it was trained on a different corpus it is hard to compare). There are several interesting and perhaps counter-intuitive results that emerge from this analysis and the authors do a nice job of examining those results and, for the most part, explaining them. However, I found the discussion of the word-order experiment rather unsatisfying. It seems to me that the appropriate question should have been something like, *How well does model X do compared to the theoretical upper bound which can be deduced from natural language statistics?* This is investigated from one angle in Section 7, but I would have preferred to the effect of natural language statistics discussed up front rather than presented as the explanation to a *surprising* observation. I had a similar reaction to the word-order experiments. Most of the interesting results, in my opinion, are about the ED model. It is fascinating that the LSTM encoder does not seem to rely on natural-language ordering statistics -- it seems like doing so should be a big win in terms of per-parameter expressivity. I also think that it*s strange that word content accuracy begins to drop for high-dimensional embeddings. I suppose this could be investigated by handicapping the decoder. Overall, this is a very nice paper investigating some aspects of the information content stored in various types of sentence embeddings. I recommend acceptance.
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>



This paper analyzes various unsupervised sentence embedding approaches by means of a set of auxiliary prediction tasks. By examining how well classifiers can predict word order, word content, and sentence length, the authors aim to assess how much and what type of information is captured by the different embedding models. The main focus is on a comparison between and encoder-decoder model (ED) and a permutation-invariant model, CBOW. (There is also an analysis of skip-thought vectors, but since it was trained on a different corpus it is hard to compare). There are several interesting and perhaps counter-intuitive results that emerge from this analysis and the authors do a nice job of examining those results and, for the most part, explaining them. However, I found the discussion of the word-order experiment rather unsatisfying. It seems to me that the appropriate question should have been something like, *How well does model X do compared to the theoretical upper bound which can be deduced from natural language statistics?* This is investigated from one angle in Section 7, but I would have preferred to the effect of natural language statistics discussed up front rather than presented as the explanation to a *surprising* observation. I had a similar reaction to the word-order experiments. Most of the interesting results, in my opinion, are about the ED model. It is fascinating that the LSTM encoder does not seem to rely on natural-language ordering statistics -- it seems like doing so should be a big win in terms of per-parameter expressivity. I also think that it*s strange that word content accuracy begins to drop for high-dimensional embeddings. I suppose this could be investigated by handicapping the decoder. Overall, this is a very nice paper investigating some aspects of the information content stored in various types of sentence embeddings. I recommend acceptance.
assistant
This paper analyzes various unsupervised sentence embedding approaches by means of a set of auxiliary prediction tasks. By examining how well classifiers can predict word order, word content, and sentence length, the authors aim to assess how much and what type of information is captured by the different embedding models. The main focus is on a comparison between and encoder-decoder model (ED) and a permutation-invariant model, CBOW. (There is also an analysis of skip-thought vectors, but since it was trained on a different corpus it is hard to compare). There are several interesting and perhaps counter-intuitive results that emerge from this analysis and the authors do a nice job of examining those results and, for the most part, explaining them. However, I found the discussion of the word-order experiment rather unsatisfying. It seems to me that the appropriate question should have been something like, *How well does model X do compared to the theoretical upper bound which can be deduced from natural language statistics? * This is investigated from one angle in Section 7, but I would have preferred to the effect of natural language statistics discussed up front rather than presented as the explanation to a *surprising* observation. I had a similar reaction to the word-order experiments. Most of the interesting results, in my opinion, are about the ED model. It is fascinating that the LSTM encoder does not seem to rely on natural-language ordering statistics -- it seems like doing so should be a big win in terms of per-parameter expressivity. I also think that it*s strange that word content accuracy begins to drop for high-dimensional embeddings. I suppose this could be investigated by handicapping the decoder. Overall, this is a very nice paper investigating some aspects of the information content stored in various types of sentence embeddings. I recommend acceptance.