ADAPTING PRETRAINED LANGUAGE MODELS FOR LONG DOCUMENT CLASSIFICATION
Pretrained language models (LMs) have shown excellent results in achieving human like performance on many language tasks. However, the most powerful LMs have one significant drawback: a fixed-sized input. With this constraint, these LMs are unable to utilize the full input of long documents. In this paper, we introduce a new framework to handle documents of arbitrary lengths. We investigate the addition of a recurrent mechanism to extend the input size and utilizing attention to identify the most discriminating segment of the input. We perform extensive validating experiments on patent and Arxiv datasets, both of which have long text. We demonstrate our method significantly outperforms state-of-the-art results reported in recent literature.
This paper proposes to compare different methods to build BERT/GPT representations of long documents, to bypass the limitation of the input size of these models. One of the proposed method uses attention mechanism to discover the most significant portion of the text which are use to backpropagate the error on the language model. Three combination methods (concatenation, RNN and attention) are tested on 2 databases plus one modified version of one of the databases to show the impact of the presentation bias in the texts (most important part are at the beginning). Results show that the largest improvement is the base BERT model over the previously proposed model : this aspect should be comment : what is the reason of the improvement ? Combination of textual part also yields improvement, but to a smaller extend. Hyper-parameter and Training/Testing time are reported, which is useful from a practical point of view if one should decide to implement the proposed method or not, considering the extra computational load and the relatively small improvement. The Shuffling experiment demonstrate an interesting behaviour of the models, that should be confirmed on a real dataset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ADAPTING PRETRAINED LANGUAGE MODELS FOR LONG DOCUMENT CLASSIFICATION
Pretrained language models (LMs) have shown excellent results in achieving human like performance on many language tasks. However, the most powerful LMs have one significant drawback: a fixed-sized input. With this constraint, these LMs are unable to utilize the full input of long documents. In this paper, we introduce a new framework to handle documents of arbitrary lengths. We investigate the addition of a recurrent mechanism to extend the input size and utilizing attention to identify the most discriminating segment of the input. We perform extensive validating experiments on patent and Arxiv datasets, both of which have long text. We demonstrate our method significantly outperforms state-of-the-art results reported in recent literature.
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 to compare different methods to build BERT/GPT representations of long documents, to bypass the limitation of the input size of these models. One of the proposed method uses attention mechanism to discover the most significant portion of the text which are use to backpropagate the error on the language model. Three combination methods (concatenation, RNN and attention) are tested on 2 databases plus one modified version of one of the databases to show the impact of the presentation bias in the texts (most important part are at the beginning). Results show that the largest improvement is the base BERT model over the previously proposed model : this aspect should be comment : what is the reason of the improvement ? Combination of textual part also yields improvement, but to a smaller extend. Hyper-parameter and Training/Testing time are reported, which is useful from a practical point of view if one should decide to implement the proposed method or not, considering the extra computational load and the relatively small improvement. The Shuffling experiment demonstrate an interesting behaviour of the models, that should be confirmed on a real dataset.
assistant
This paper proposes to compare different methods to build BERT/GPT representations of long documents, to bypass the limitation of the input size of these models. One of the proposed method uses attention mechanism to discover the most significant portion of the text which are use to backpropagate the error on the language model. Three combination methods (concatenation, RNN and attention) are tested on 2 databases plus one modified version of one of the databases to show the impact of the presentation bias in the texts (most important part are at the beginning). Results show that the largest improvement is the base BERT model over the previously proposed model : this aspect should be comment : what is the reason of the improvement ? Combination of textual part also yields improvement, but to a smaller extend. Hyper-parameter and Training/Testing time are reported, which is useful from a practical point of view if one should decide to implement the proposed method or not, considering the extra computational load and the relatively small improvement. The Shuffling experiment demonstrate an interesting behaviour of the models, that should be confirmed on a real dataset.