Attention over Phrases
How to represent the sentence ``That*s the last straw for her**? The answer of the self-attention is a weighted sum of each individual words, i.e. . But the weighted sum of ``That*s**, ``the**, ``last**, ``straw** can hardly represent the semantics of the phrase. We argue that the phrases play an important role in attention. If we combine some words into phrases, a more reasonable representation with compositions is . While recent studies prefer to use the attention mechanism to represent the natural language, few noticed the word compositions. In this paper, we study the problem of representing such compositional attentions in phrases. In this paper, we proposed a new attention architecture called HyperTransformer. Besides representing the words of the sentence, we introduce hypernodes to represent the candidate phrases in attention. HyperTransformer has two phases. The first phase is used to attend over all word/phrase pairs, which is similar to the standard Transformer. The second phase is used to represent the inductive bias within each phrase. Specially, we incorporate the non-linear attention in the second phase. The non-linearity represents the the semantic mutations in phrases. The experimental performance has been greatly improved. In WMT16 English-German translation task, the BLEU increases from 20.90 (by Transformer) to 34.61 (by HyperTransformer).
I think the paper needs a deep review in the English part. For example, in the abstract, they repeat *In this paper* a couple of time and it is complicated to understand the introduction and methodology. Also, I think the paper needs a better structure. The related work should be first in order to understand the relevance of this paper. From the experiment point of view, it is necessary a better explanation about the hyperparameters or the experiments which were carried out. In addition, the single database was used to evaluate the technology which is not enough to show the big different respect to the transformed paper. Also, the comparison is not really fair. In each *layer* of the proposed phrase transformer, it has actually two self-attention layers, but the baseline has only one self-attention layer. In addition more methodologies should be necessary to compare the results of the experiment. The architecture part is complicated to follow and I don*t understand the big contribution of this paper. For that reason, I recommend a reject the paper and work more for the final version
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Attention over Phrases
How to represent the sentence ``That*s the last straw for her**? The answer of the self-attention is a weighted sum of each individual words, i.e. . But the weighted sum of ``That*s**, ``the**, ``last**, ``straw** can hardly represent the semantics of the phrase. We argue that the phrases play an important role in attention. If we combine some words into phrases, a more reasonable representation with compositions is . While recent studies prefer to use the attention mechanism to represent the natural language, few noticed the word compositions. In this paper, we study the problem of representing such compositional attentions in phrases. In this paper, we proposed a new attention architecture called HyperTransformer. Besides representing the words of the sentence, we introduce hypernodes to represent the candidate phrases in attention. HyperTransformer has two phases. The first phase is used to attend over all word/phrase pairs, which is similar to the standard Transformer. The second phase is used to represent the inductive bias within each phrase. Specially, we incorporate the non-linear attention in the second phase. The non-linearity represents the the semantic mutations in phrases. The experimental performance has been greatly improved. In WMT16 English-German translation task, the BLEU increases from 20.90 (by Transformer) to 34.61 (by HyperTransformer).
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>



I think the paper needs a deep review in the English part. For example, in the abstract, they repeat *In this paper* a couple of time and it is complicated to understand the introduction and methodology. Also, I think the paper needs a better structure. The related work should be first in order to understand the relevance of this paper. From the experiment point of view, it is necessary a better explanation about the hyperparameters or the experiments which were carried out. In addition, the single database was used to evaluate the technology which is not enough to show the big different respect to the transformed paper. Also, the comparison is not really fair. In each *layer* of the proposed phrase transformer, it has actually two self-attention layers, but the baseline has only one self-attention layer. In addition more methodologies should be necessary to compare the results of the experiment. The architecture part is complicated to follow and I don*t understand the big contribution of this paper. For that reason, I recommend a reject the paper and work more for the final version
assistant
I think the paper needs a deep review in the English part. For example, in the abstract, they repeat *In this paper* a couple of time and it is complicated to understand the introduction and methodology. Also, I think the paper needs a better structure. The related work should be first in order to understand the relevance of this paper. From the experiment point of view, it is necessary a better explanation about the hyperparameters or the experiments which were carried out. In addition, the single database was used to evaluate the technology which is not enough to show the big different respect to the transformed paper. Also, the comparison is not really fair. In each *layer* of the proposed phrase transformer, it has actually two self-attention layers, but the baseline has only one self-attention layer. In addition more methodologies should be necessary to compare the results of the experiment. The architecture part is complicated to follow and I don*t understand the big contribution of this paper. For that reason, I recommend a reject the paper and work more for the final version