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.
The paper presents a methods to combine multiple pre-trained language models using an attention mechanism in order to take the whole document into account. The effectiveness of the methods is evaluated on two long document classification tasks (Arxiv publication and patent classification) with state-of the art results. Pros: - the effectiveness of the methods is experimentally demonstrated using two relevant datasets - the inverted wireless experiment clearly shows the interest of the attention combination strategy Cons: - the methods is very simple (combining multiple segment prediction to perform document classifications) making the contribution of this paper quite weak. In the own terms of the authors, the *main contribution [...] is to investigate the effectiveness of different combination strategies*. I am not sure that it is sufficient for the ICLR standards. - the paper is difficult to read and should be proofread to improve readability Minor issues: - Socher et al (2011) uses recursive neural networks (ReNN) and not recurrent neural networks (RNN). RNN are ReNN but restrained to a linear chain structure - In Introduction: ... in the domain extremely complex data that is language ... -> I*m not sure the sentence is correct - In Introduction: the last sentence should be shorten and rephrased. - many typos
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>



The paper presents a methods to combine multiple pre-trained language models using an attention mechanism in order to take the whole document into account. The effectiveness of the methods is evaluated on two long document classification tasks (Arxiv publication and patent classification) with state-of the art results. Pros: - the effectiveness of the methods is experimentally demonstrated using two relevant datasets - the inverted wireless experiment clearly shows the interest of the attention combination strategy Cons: - the methods is very simple (combining multiple segment prediction to perform document classifications) making the contribution of this paper quite weak. In the own terms of the authors, the *main contribution [...] is to investigate the effectiveness of different combination strategies*. I am not sure that it is sufficient for the ICLR standards. - the paper is difficult to read and should be proofread to improve readability Minor issues: - Socher et al (2011) uses recursive neural networks (ReNN) and not recurrent neural networks (RNN). RNN are ReNN but restrained to a linear chain structure - In Introduction: ... in the domain extremely complex data that is language ... -> I*m not sure the sentence is correct - In Introduction: the last sentence should be shorten and rephrased. - many typos
assistant
The paper presents a methods to combine multiple pre-trained language models using an attention mechanism in order to take the whole document into account. The effectiveness of the methods is evaluated on two long document classification tasks (Arxiv publication and patent classification) with state-of the art results. Pros: - the effectiveness of the methods is experimentally demonstrated using two relevant datasets - the inverted wireless experiment clearly shows the interest of the attention combination strategy Cons: - the methods is very simple (combining multiple segment prediction to perform document classifications) making the contribution of this paper quite weak. In the own terms of the authors, the *main contribution [...] is to investigate the effectiveness of different combination strategies*. I am not sure that it is sufficient for the ICLR standards. - the paper is difficult to read and should be proofread to improve readability Minor issues: - Socher et al (2011) uses recursive neural networks (ReNN) and not recurrent neural networks (RNN). RNN are ReNN but restrained to a linear chain structure - In Introduction: ... in the domain extremely complex data that is language ... -> I*m not sure the sentence is correct - In Introduction: the last sentence should be shorten and rephrased. - many typos