Interpretable Network Structure for Modeling Contextual Dependency
Neural language models have achieved great success in many NLP tasks, to a large extent, due to the ability to capture contextual dependencies among terms in a text. While many efforts have been devoted to empirically explain the connection between the network hyperparameters and the ability to represent the contextual dependency, the theoretical analysis is relatively insufficient. Inspired by the recent research on the use of tensor space to explain the neural network architecture, we explore the interpretable mechanism for neural language models. Specifically, we define the concept of separation rank in the language modeling process, in order to theoretically measure the degree of contextual dependencies in a sentence. Then, we show that the lower bound of such a separation rank can reveal the quantitative relation between the network structure (e.g. depth/width) and the modeling ability for the contextual dependency. Especially, increasing the depth of the neural network can be more effective to improve the ability of modeling contextual dependency. Therefore, it is important to design an adaptive network to compute the adaptive depth in a task. Inspired by Adaptive Computation Time (ACT), we design an adaptive recurrent network based on the separation rank to model contextual dependency. Experiments on various NLP tasks have verified the proposed theoretical analysis. We also test our adaptive recurrent neural network in the sentence classification task, and the experiments show that it can achieve better results than the traditional bidirectional LSTM.
This paper derives lower bounds on the separation rank of a wider class of recurrent NLP models in terms of its depth and number of hidden layers, demonstrating that both the number of hidden units as well as the number of layers improves the ability of NLP networks to model context dependency. It then introduces a novel bidirectional NLP variant that is supposed to capture a good trade-off between computational cost and performance. The manuscript is very dense and does not follow a straight and easy-to-follow story line. In particular, the introduction of the bidirectional variant seems to substantially distract from the main story line of the paper (there is also no connection between the theoretical results to the bidirectional network). The improvements of the bidirectional models also seem to be minor, but no standard deviations for the performance results are reported. A clear description as to which language models are captured by the TSLM model is missing. Also, it is unclear how tight the bounds actually are given that no value for m (the word length) is given. Finally, the title does not reflect the content of the paper (there is nothing interpretable about the network structure).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Interpretable Network Structure for Modeling Contextual Dependency
Neural language models have achieved great success in many NLP tasks, to a large extent, due to the ability to capture contextual dependencies among terms in a text. While many efforts have been devoted to empirically explain the connection between the network hyperparameters and the ability to represent the contextual dependency, the theoretical analysis is relatively insufficient. Inspired by the recent research on the use of tensor space to explain the neural network architecture, we explore the interpretable mechanism for neural language models. Specifically, we define the concept of separation rank in the language modeling process, in order to theoretically measure the degree of contextual dependencies in a sentence. Then, we show that the lower bound of such a separation rank can reveal the quantitative relation between the network structure (e.g. depth/width) and the modeling ability for the contextual dependency. Especially, increasing the depth of the neural network can be more effective to improve the ability of modeling contextual dependency. Therefore, it is important to design an adaptive network to compute the adaptive depth in a task. Inspired by Adaptive Computation Time (ACT), we design an adaptive recurrent network based on the separation rank to model contextual dependency. Experiments on various NLP tasks have verified the proposed theoretical analysis. We also test our adaptive recurrent neural network in the sentence classification task, and the experiments show that it can achieve better results than the traditional bidirectional LSTM.
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 derives lower bounds on the separation rank of a wider class of recurrent NLP models in terms of its depth and number of hidden layers, demonstrating that both the number of hidden units as well as the number of layers improves the ability of NLP networks to model context dependency. It then introduces a novel bidirectional NLP variant that is supposed to capture a good trade-off between computational cost and performance. The manuscript is very dense and does not follow a straight and easy-to-follow story line. In particular, the introduction of the bidirectional variant seems to substantially distract from the main story line of the paper (there is also no connection between the theoretical results to the bidirectional network). The improvements of the bidirectional models also seem to be minor, but no standard deviations for the performance results are reported. A clear description as to which language models are captured by the TSLM model is missing. Also, it is unclear how tight the bounds actually are given that no value for m (the word length) is given. Finally, the title does not reflect the content of the paper (there is nothing interpretable about the network structure).
assistant
This paper derives lower bounds on the separation rank of a wider class of recurrent NLP models in terms of its depth and number of hidden layers, demonstrating that both the number of hidden units as well as the number of layers improves the ability of NLP networks to model context dependency. It then introduces a novel bidirectional NLP variant that is supposed to capture a good trade-off between computational cost and performance. The manuscript is very dense and does not follow a straight and easy-to-follow story line. In particular, the introduction of the bidirectional variant seems to substantially distract from the main story line of the paper (there is also no connection between the theoretical results to the bidirectional network). The improvements of the bidirectional models also seem to be minor, but no standard deviations for the performance results are reported. A clear description as to which language models are captured by the TSLM model is missing. Also, it is unclear how tight the bounds actually are given that no value for m (the word length) is given. Finally, the title does not reflect the content of the paper (there is nothing interpretable about the network structure).