Representation Degeneration Problem in Training Natural Language Generation Models
We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the emph{representation degeneration problem}. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms.
 This work proposes a simple regularization term which penalize cosine similarity of word embedding parameters in the loss function. The motivation comes from empirical studies of word embedding parameters in three tasks, translation, word2vec and classification, and showed that the parameters for the translation task are not distributed when compared with other tasks. The problem is hypothesized by the rare word problem especially when parameters are tied for softmax and input embedding, and proposes a cosine similarity regularization. Experiments on English/German show consistent gains over non-regularized loss. Pros: - The proposed method is well motivated from empirical studies by visualizing parameters of three tasks, and the analysis on rare words are convincing. - Good performance in language modeling and translation tasks by incorporating the proposed regularization. Cons: - The visualization might be slightly miss leading in that the size of classification, e.g., the vocabulary size, is different, e.g., BPE for translation, word for word2vec and categories of MNIST. I*d also like to see visualization for comparable experiments, e.g., language modeling with or without tied parameters. - Given that BPE is used in translation, the analysis might not hold since rare words would not occur very frequently, and thus, the gain might come from other factors, e.g., tied source/target embedding parameters in Transformer. - I*d like to see experiments under un-tided parameters with the proposed regularization.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Representation Degeneration Problem in Training Natural Language Generation Models
We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the emph{representation degeneration problem}. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms.
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 work proposes a simple regularization term which penalize cosine similarity of word embedding parameters in the loss function. The motivation comes from empirical studies of word embedding parameters in three tasks, translation, word2vec and classification, and showed that the parameters for the translation task are not distributed when compared with other tasks. The problem is hypothesized by the rare word problem especially when parameters are tied for softmax and input embedding, and proposes a cosine similarity regularization. Experiments on English/German show consistent gains over non-regularized loss. Pros: - The proposed method is well motivated from empirical studies by visualizing parameters of three tasks, and the analysis on rare words are convincing. - Good performance in language modeling and translation tasks by incorporating the proposed regularization. Cons: - The visualization might be slightly miss leading in that the size of classification, e.g., the vocabulary size, is different, e.g., BPE for translation, word for word2vec and categories of MNIST. I*d also like to see visualization for comparable experiments, e.g., language modeling with or without tied parameters. - Given that BPE is used in translation, the analysis might not hold since rare words would not occur very frequently, and thus, the gain might come from other factors, e.g., tied source/target embedding parameters in Transformer. - I*d like to see experiments under un-tided parameters with the proposed regularization.
assistant
 This work proposes a simple regularization term which penalize cosine similarity of word embedding parameters in the loss function. The motivation comes from empirical studies of word embedding parameters in three tasks, translation, word2vec and classification, and showed that the parameters for the translation task are not distributed when compared with other tasks. The problem is hypothesized by the rare word problem especially when parameters are tied for softmax and input embedding, and proposes a cosine similarity regularization. Experiments on English/German show consistent gains over non-regularized loss. Pros: - The proposed method is well motivated from empirical studies by visualizing parameters of three tasks, and the analysis on rare words are convincing. - Good performance in language modeling and translation tasks by incorporating the proposed regularization. Cons: - The visualization might be slightly miss leading in that the size of classification, e.g., the vocabulary size, is different, e.g., BPE for translation, word for word2vec and categories of MNIST. I*d also like to see visualization for comparable experiments, e.g., language modeling with or without tied parameters. - Given that BPE is used in translation, the analysis might not hold since rare words would not occur very frequently, and thus, the gain might come from other factors, e.g., tied source/target embedding parameters in Transformer. - I*d like to see experiments under un-tided parameters with the proposed regularization.