Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks. However, contextualized embeddings at the output layer of these powerful models tend to degenerate and occupy an anisotropic cone in the vector space, which is called the representation degeneration problem. In this paper, we propose a novel spectrum control approach to address this degeneration problem. The core idea of our method is to directly guide the spectra training of the output embedding matrix with a slow-decaying singular value prior distribution through a reparameterization framework. We show that our proposed method encourages isotropy of the learned word representations while maintains the modeling power of these contextual neural models. We further provide a theoretical analysis and insight on the benefit of modeling singular value distribution. We demonstrate that our spectrum control method outperforms the state-of-the-art Transformer-XL modeling for language model, and various Transformer-based models for machine translation, on common benchmark datasets for these tasks.
Authors propose to apply Spectrum control regularization to the embedding of weight matrices in NLP problems such as language modeling and neural machine translation. Spectrum Control Regularization was originally proposed and applied to GANs (Jiang et al 2019) The author motivate the approach by showing that the singular values of embedding weight matrices, although I am not convinced that it is such a big issue. In terms of experimental results authors show a very slight improvement over strong baseline models, that further shows an evidence that regularization singular values of embedding matrices is not very important. Overall the paper is written well, however the contribution is very marginal.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks. However, contextualized embeddings at the output layer of these powerful models tend to degenerate and occupy an anisotropic cone in the vector space, which is called the representation degeneration problem. In this paper, we propose a novel spectrum control approach to address this degeneration problem. The core idea of our method is to directly guide the spectra training of the output embedding matrix with a slow-decaying singular value prior distribution through a reparameterization framework. We show that our proposed method encourages isotropy of the learned word representations while maintains the modeling power of these contextual neural models. We further provide a theoretical analysis and insight on the benefit of modeling singular value distribution. We demonstrate that our spectrum control method outperforms the state-of-the-art Transformer-XL modeling for language model, and various Transformer-based models for machine translation, on common benchmark datasets for these tasks.
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>



Authors propose to apply Spectrum control regularization to the embedding of weight matrices in NLP problems such as language modeling and neural machine translation. Spectrum Control Regularization was originally proposed and applied to GANs (Jiang et al 2019) The author motivate the approach by showing that the singular values of embedding weight matrices, although I am not convinced that it is such a big issue. In terms of experimental results authors show a very slight improvement over strong baseline models, that further shows an evidence that regularization singular values of embedding matrices is not very important. Overall the paper is written well, however the contribution is very marginal.
assistant
Authors propose to apply Spectrum control regularization to the embedding of weight matrices in NLP problems such as language modeling and neural machine translation. Spectrum Control Regularization was originally proposed and applied to GANs (Jiang et al 2019) The author motivate the approach by showing that the singular values of embedding weight matrices, although I am not convinced that it is such a big issue. In terms of experimental results authors show a very slight improvement over strong baseline models, that further shows an evidence that regularization singular values of embedding matrices is not very important. Overall the paper is written well, however the contribution is very marginal.