Sentence embedding with contrastive multi-views learning
In this work, we propose a self-supervised method to learn sentence representations with an injection of linguistic knowledge. Multiple linguistic frameworks propose diverse sentence structures from which semantic meaning might be expressed out of compositional words operations. We aim to take advantage of this linguist diversity and learn to represent sentences by contrasting these diverse views. Formally, multiple views of the same sentence are mapped to close representations. On the contrary, views from other sentences are mapped further. By contrasting different linguistic views, we aim at building embeddings which better capture semantic and which are less sensitive to the sentence outward form.
This paper describes a self-supervised sentence embedding approach that incorporates a different view from plain text where some extent of linguistic knowledge is incorporated through the application of tree LSTM. The training procedure is standard contrastive framework where the model is encouraged to distinguish between context sentence (sentences appearing close to the target sentence) and negative samples. Evaluations are conducted on 1) downstream tasks, but with a simple logistic regression model on top of sentence embeddings; 2) probing tasks that more focus on surface information prediction, syntactic and semantic tasks; and 3) qualitative analysis with nearest 5 sentences. Although the experiments are thorough, I am in favor of rejecting this paper with the following reasons: First, the proposed model is trained with 4.6M sentences among 78M available for 33 hours. It is unclear why authors stop the training at this early stage but the results on all three evaluations seem to be inferior to the state-of-the-art by a big margin. I am happy to raise my score if authors can show the results of a well trained proposed model. Second, the paper has some room for improvement in terms of clarity, to name a few: 1) Authors can strengthen the motivation for multi-views learning in related work; 2) Formula 1 for softmax is wrong; 3) Contrastive LSTM and contrastive tree LSTM are not clearly defined in the paper, although the former should refer to quick-thoughts and the latter means the proposed method; 4) In qualitative analysis, for the last example, there is exactly the same candidate with similarity score 0.012. According to cosine similarity, wouldn’t this be 0 and also show up in the baseline model regardless of the embeddings?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sentence embedding with contrastive multi-views learning
In this work, we propose a self-supervised method to learn sentence representations with an injection of linguistic knowledge. Multiple linguistic frameworks propose diverse sentence structures from which semantic meaning might be expressed out of compositional words operations. We aim to take advantage of this linguist diversity and learn to represent sentences by contrasting these diverse views. Formally, multiple views of the same sentence are mapped to close representations. On the contrary, views from other sentences are mapped further. By contrasting different linguistic views, we aim at building embeddings which better capture semantic and which are less sensitive to the sentence outward form.
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 describes a self-supervised sentence embedding approach that incorporates a different view from plain text where some extent of linguistic knowledge is incorporated through the application of tree LSTM. The training procedure is standard contrastive framework where the model is encouraged to distinguish between context sentence (sentences appearing close to the target sentence) and negative samples. Evaluations are conducted on 1) downstream tasks, but with a simple logistic regression model on top of sentence embeddings; 2) probing tasks that more focus on surface information prediction, syntactic and semantic tasks; and 3) qualitative analysis with nearest 5 sentences. Although the experiments are thorough, I am in favor of rejecting this paper with the following reasons: First, the proposed model is trained with 4.6M sentences among 78M available for 33 hours. It is unclear why authors stop the training at this early stage but the results on all three evaluations seem to be inferior to the state-of-the-art by a big margin. I am happy to raise my score if authors can show the results of a well trained proposed model. Second, the paper has some room for improvement in terms of clarity, to name a few: 1) Authors can strengthen the motivation for multi-views learning in related work; 2) Formula 1 for softmax is wrong; 3) Contrastive LSTM and contrastive tree LSTM are not clearly defined in the paper, although the former should refer to quick-thoughts and the latter means the proposed method; 4) In qualitative analysis, for the last example, there is exactly the same candidate with similarity score 0.012. According to cosine similarity, wouldn’t this be 0 and also show up in the baseline model regardless of the embeddings?
assistant
This paper describes a self-supervised sentence embedding approach that incorporates a different view from plain text where some extent of linguistic knowledge is incorporated through the application of tree LSTM. The training procedure is standard contrastive framework where the model is encouraged to distinguish between context sentence (sentences appearing close to the target sentence) and negative samples. Evaluations are conducted on 1) downstream tasks, but with a simple logistic regression model on top of sentence embeddings; 2) probing tasks that more focus on surface information prediction, syntactic and semantic tasks; and 3) qualitative analysis with nearest 5 sentences. Although the experiments are thorough, I am in favor of rejecting this paper with the following reasons: First, the proposed model is trained with 4.6M sentences among 78M available for 33 hours. It is unclear why authors stop the training at this early stage but the results on all three evaluations seem to be inferior to the state-of-the-art by a big margin. I am happy to raise my score if authors can show the results of a well trained proposed model. Second, the paper has some room for improvement in terms of clarity, to name a few: 1) Authors can strengthen the motivation for multi-views learning in related work; 2) Formula 1 for softmax is wrong; 3) Contrastive LSTM and contrastive tree LSTM are not clearly defined in the paper, although the former should refer to quick-thoughts and the latter means the proposed method; 4) In qualitative analysis, for the last example, there is exactly the same candidate with similarity score 0.012. According to cosine similarity, wouldn’t this be 0 and also show up in the baseline model regardless of the embeddings?