CaptainGAN: Navigate Through Embedding Space For Better Text Generation
Score-function-based text generation approaches such as REINFORCE, in general, suffer from high computational complexity and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a reward function and ignore the gradient information. In this paper, we propose a novel approach, CaptainGAN, which adopts the straight-through gradient estimator and introduces a ”re-centered” gradient estimation technique to steer the generator toward better text tokens through the embedding space. Our method is stable to train and converges quickly without maximum likelihood pre-training. On multiple metrics of text quality and diversity, our method outperforms existing GAN-based methods on natural language generation.
The authors propose CaptainGAN, a method using the straight-through gradient estimator to improve training of the generator for text generation. The paper is well-written and the evaluation seems thorough, comparing to relevant baselines. Comments: Figure 3: the caption refers to Caccia et al. for results on LeakGAN, MaliGAN and seqGAN, but unless I’ve missed it, RelGAN hasn’t yet been introduced by name as a baseline? The citation is given in the opening part of the introduction, in an enumeration, but isn’t revisited later in the text - not even here where the results of the model are introduced. Given that it seems, according to the presented results, to be the most competitive of the GAN models that the authors are comparing to, maybe it’s worth adding more contextual information on RelGAN to the Background section? For their method, the authors should report an average performance over several random seeds and provide the standard deviation / confidence intervals, for the readers to be able to assess the stability of the method and the significance of the improvement reported in the results. I find Section 5.5. particularly interesting, as well as the reported perplexity in Table 2. The authors provide 3 bullet points to explain the unusually high perplexity of the generator on the training and validation data. I feel that the explanations that are given are at the moment vague and not visibly backed by data, therefore being speculative. Obviously, point 1) is hard to quantify - but point 2) could possibly be at least partially quantified - if the hypothesis is that names, places, punctuation marks etc play an important role in the reported perplexity score, then maybe the authors could test this by correlating model perplexity on sentences with whether those sentences contain these types of words?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CaptainGAN: Navigate Through Embedding Space For Better Text Generation
Score-function-based text generation approaches such as REINFORCE, in general, suffer from high computational complexity and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a reward function and ignore the gradient information. In this paper, we propose a novel approach, CaptainGAN, which adopts the straight-through gradient estimator and introduces a ”re-centered” gradient estimation technique to steer the generator toward better text tokens through the embedding space. Our method is stable to train and converges quickly without maximum likelihood pre-training. On multiple metrics of text quality and diversity, our method outperforms existing GAN-based methods on natural language generation.
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 authors propose CaptainGAN, a method using the straight-through gradient estimator to improve training of the generator for text generation. The paper is well-written and the evaluation seems thorough, comparing to relevant baselines. Comments: Figure 3: the caption refers to Caccia et al. for results on LeakGAN, MaliGAN and seqGAN, but unless I’ve missed it, RelGAN hasn’t yet been introduced by name as a baseline? The citation is given in the opening part of the introduction, in an enumeration, but isn’t revisited later in the text - not even here where the results of the model are introduced. Given that it seems, according to the presented results, to be the most competitive of the GAN models that the authors are comparing to, maybe it’s worth adding more contextual information on RelGAN to the Background section? For their method, the authors should report an average performance over several random seeds and provide the standard deviation / confidence intervals, for the readers to be able to assess the stability of the method and the significance of the improvement reported in the results. I find Section 5.5. particularly interesting, as well as the reported perplexity in Table 2. The authors provide 3 bullet points to explain the unusually high perplexity of the generator on the training and validation data. I feel that the explanations that are given are at the moment vague and not visibly backed by data, therefore being speculative. Obviously, point 1) is hard to quantify - but point 2) could possibly be at least partially quantified - if the hypothesis is that names, places, punctuation marks etc play an important role in the reported perplexity score, then maybe the authors could test this by correlating model perplexity on sentences with whether those sentences contain these types of words?
assistant
The authors propose CaptainGAN, a method using the straight-through gradient estimator to improve training of the generator for text generation. The paper is well-written and the evaluation seems thorough, comparing to relevant baselines. Comments: Figure 3: the caption refers to Caccia et al. for results on LeakGAN, MaliGAN and seqGAN, but unless I’ve missed it, RelGAN hasn’t yet been introduced by name as a baseline? The citation is given in the opening part of the introduction, in an enumeration, but isn’t revisited later in the text - not even here where the results of the model are introduced. Given that it seems, according to the presented results, to be the most competitive of the GAN models that the authors are comparing to, maybe it’s worth adding more contextual information on RelGAN to the Background section? For their method, the authors should report an average performance over several random seeds and provide the standard deviation / confidence intervals, for the readers to be able to assess the stability of the method and the significance of the improvement reported in the results. I find Section 5.5. particularly interesting, as well as the reported perplexity in Table 2. The authors provide 3 bullet points to explain the unusually high perplexity of the generator on the training and validation data. I feel that the explanations that are given are at the moment vague and not visibly backed by data, therefore being speculative. Obviously, point 1) is hard to quantify - but point 2) could possibly be at least partially quantified - if the hypothesis is that names, places, punctuation marks etc play an important role in the reported perplexity score, then maybe the authors could test this by correlating model perplexity on sentences with whether those sentences contain these types of words?