Depth-Adaptive Transformer
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.
The authors study depth-adaptive Transformer models and show that they can perform well even under fairly basic strategies to stop the Transformer early. The paper is well written and the results convincing. There is not a lot of novelty but the results hold well, so it is a clear contribution. One main issue that prevents this reviewer from increasing the rating is the measure of speed the authors use: counting exit at every token. This is a fine measure for inference time, but during training the model proceeds on the whole sequence, right? Could you provide speed numbers for the training step? Is there any improvement over a baseline Transformer or is this technique solely for inference? (Which is still a contribution, but it should be made clear in the paper.) I thank the authors for the response and clarification. I stand by my score in the light of it.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Depth-Adaptive Transformer
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.
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>



The authors study depth-adaptive Transformer models and show that they can perform well even under fairly basic strategies to stop the Transformer early. The paper is well written and the results convincing. There is not a lot of novelty but the results hold well, so it is a clear contribution. One main issue that prevents this reviewer from increasing the rating is the measure of speed the authors use: counting exit at every token. This is a fine measure for inference time, but during training the model proceeds on the whole sequence, right? Could you provide speed numbers for the training step? Is there any improvement over a baseline Transformer or is this technique solely for inference? (Which is still a contribution, but it should be made clear in the paper.) I thank the authors for the response and clarification. I stand by my score in the light of it.
assistant
The authors study depth-adaptive Transformer models and show that they can perform well even under fairly basic strategies to stop the Transformer early. The paper is well written and the results convincing. There is not a lot of novelty but the results hold well, so it is a clear contribution. One main issue that prevents this reviewer from increasing the rating is the measure of speed the authors use: counting exit at every token. This is a fine measure for inference time, but during training the model proceeds on the whole sequence, right? Could you provide speed numbers for the training step? Is there any improvement over a baseline Transformer or is this technique solely for inference? (Which is still a contribution, but it should be made clear in the paper.) I thank the authors for the response and clarification. I stand by my score in the light of it.