EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS
Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset.
 This paper proposes a method to find an effective structure of RNNs and attention mechanisms by searching programs over the stack-oriented execution engine. Although the new point in this paper looks only the representation paradigm of each program: (possibly variable length) list of the function applications, that could be a flexible framework to find a function without any prior structures like Fig.1-left. However, the design of the execution engine looks not well-designed. E.g., authors described that the engine ignores the binary operations that could not be executed at the time. But in my thought, such operations should not be included in the set of candidate operations, i.e., the set of candidates should be constrained directly by the state of the stack. Also, including repeating "identity" operations (in the candidates of attention operations) seems that some unnecessary redundancy is introduced into the search space. The same expressiveness could be achieved by predicting a special token only once at the end of the sequence (namely, "end-of-sequence" token as just same as usual auto-regressive RNN-based decoder models). Comparison in experiments looks meaningless. Score improvement is slight nevertheless authors paid much computation cost for searching accurate network structures. The conventional method (Zoph&Le,17) in row 3 of Table 1 looks not comparable with proposed methods because it is trained by an out-of-domain task (LM) using conventional (tree-based) search space. Authors should at least show the result by applying the conventional search space to the tasks of this paper. In Table 2, the "our baseline" looks cheap because the dot product is the least attention model in those proposed in past studies. The catastrophic score drop in the rows 5 and 7 in Table 1 looks interesting, but the paper does not show enough comprehension about this phenomenon, which makes the proposed method hard to apply other tasks. The same problem exists in the setting of the hyperparameters in the reward functions. According to the footnote, there are largely different settings about the value of .beta, which suggest a sensitivity by changing this parameter. Authors should provide some criterion to choose these hyperparameters.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS
Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset.
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 proposes a method to find an effective structure of RNNs and attention mechanisms by searching programs over the stack-oriented execution engine. Although the new point in this paper looks only the representation paradigm of each program: (possibly variable length) list of the function applications, that could be a flexible framework to find a function without any prior structures like Fig.1-left. However, the design of the execution engine looks not well-designed. E.g., authors described that the engine ignores the binary operations that could not be executed at the time. But in my thought, such operations should not be included in the set of candidate operations, i.e., the set of candidates should be constrained directly by the state of the stack. Also, including repeating "identity" operations (in the candidates of attention operations) seems that some unnecessary redundancy is introduced into the search space. The same expressiveness could be achieved by predicting a special token only once at the end of the sequence (namely, "end-of-sequence" token as just same as usual auto-regressive RNN-based decoder models). Comparison in experiments looks meaningless. Score improvement is slight nevertheless authors paid much computation cost for searching accurate network structures. The conventional method (Zoph&Le,17) in row 3 of Table 1 looks not comparable with proposed methods because it is trained by an out-of-domain task (LM) using conventional (tree-based) search space. Authors should at least show the result by applying the conventional search space to the tasks of this paper. In Table 2, the "our baseline" looks cheap because the dot product is the least attention model in those proposed in past studies. The catastrophic score drop in the rows 5 and 7 in Table 1 looks interesting, but the paper does not show enough comprehension about this phenomenon, which makes the proposed method hard to apply other tasks. The same problem exists in the setting of the hyperparameters in the reward functions. According to the footnote, there are largely different settings about the value of .beta, which suggest a sensitivity by changing this parameter. Authors should provide some criterion to choose these hyperparameters.
assistant
 This paper proposes a method to find an effective structure of RNNs and attention mechanisms by searching programs over the stack-oriented execution engine. Although the new point in this paper looks only the representation paradigm of each program: (possibly variable length) list of the function applications, that could be a flexible framework to find a function without any prior structures like Fig.1-left. However, the design of the execution engine looks not well-designed. E.g., authors described that the engine ignores the binary operations that could not be executed at the time. But in my thought, such operations should not be included in the set of candidate operations, i.e., the set of candidates should be constrained directly by the state of the stack. Also, including repeating "identity" operations (in the candidates of attention operations) seems that some unnecessary redundancy is introduced into the search space. The same expressiveness could be achieved by predicting a special token only once at the end of the sequence (namely, "end-of-sequence" token as just same as usual auto-regressive RNN-based decoder models). Comparison in experiments looks meaningless. Score improvement is slight nevertheless authors paid much computation cost for searching accurate network structures. The conventional method (Zoph&Le,17) in row 3 of Table 1 looks not comparable with proposed methods because it is trained by an out-of-domain task (LM) using conventional (tree-based) search space. Authors should at least show the result by applying the conventional search space to the tasks of this paper. In Table 2, the "our baseline" looks cheap because the dot product is the least attention model in those proposed in past studies. The catastrophic score drop in the rows 5 and 7 in Table 1 looks interesting, but the paper does not show enough comprehension about this phenomenon, which makes the proposed method hard to apply other tasks. The same problem exists in the setting of the hyperparameters in the reward functions. According to the footnote, there are largely different settings about the value of .beta, which suggest a sensitivity by changing this parameter. Authors should provide some criterion to choose these hyperparameters.