A Simple but Tough-to-Beat Baseline for Sentence Embeddings
The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, Wieting et al (ICLR*16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The method of Wieting et al. requires retraining with a substantial labeled dataset such as Paraphrase Database (Ganitkevitch et al., 2013). The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD. This weighting improves performance by about 10% to 30% in textual similarity tasks, and beats sophisticated supervised methods including RNN*s and LSTM*s. It even improves Wieting et al.*s embeddings. This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent. The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in Arora et al. (TACL*16) with new *smoothing* terms that allow for words occurring out of context, as well as high probabilities for words like and, not in all contexts.
This paper presents a new theoretically-principled method of representing sentences as vectors. The experiments show that vectors produced by this method perform well on similarity and entailment benchmarks, surpassing some RNN-based methods too. Overall, this is an interesting empirical result, especially since the model is not order-sensitive (as far as I can tell). I would like to see some more discussion on why such a simple model does better than LSTMs at capturing similarity and entailment. Could this be an artifact of these benchmarks?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Simple but Tough-to-Beat Baseline for Sentence Embeddings
The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, Wieting et al (ICLR*16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The method of Wieting et al. requires retraining with a substantial labeled dataset such as Paraphrase Database (Ganitkevitch et al., 2013). The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD. This weighting improves performance by about 10% to 30% in textual similarity tasks, and beats sophisticated supervised methods including RNN*s and LSTM*s. It even improves Wieting et al.*s embeddings. This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent. The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in Arora et al. (TACL*16) with new *smoothing* terms that allow for words occurring out of context, as well as high probabilities for words like and, not in all contexts.
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 presents a new theoretically-principled method of representing sentences as vectors. The experiments show that vectors produced by this method perform well on similarity and entailment benchmarks, surpassing some RNN-based methods too. Overall, this is an interesting empirical result, especially since the model is not order-sensitive (as far as I can tell). I would like to see some more discussion on why such a simple model does better than LSTMs at capturing similarity and entailment. Could this be an artifact of these benchmarks?
assistant
This paper presents a new theoretically-principled method of representing sentences as vectors. The experiments show that vectors produced by this method perform well on similarity and entailment benchmarks, surpassing some RNN-based methods too. Overall, this is an interesting empirical result, especially since the model is not order-sensitive (as far as I can tell). I would like to see some more discussion on why such a simple model does better than LSTMs at capturing similarity and entailment. Could this be an artifact of these benchmarks?