Bilingual-GAN: Neural Text Generation and Neural Machine Translation as Two Sides of the Same Coin
Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive results in text generation and Unsupervised NMT respectively. Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is adversarially constrained to be shared between both languages. First an NMT model is trained, with back-translation and an adversarial setup, to enforce a latent state between the two languages. The encoder and decoder are shared for the two translation directions. Next, a GAN is trained to generate ‘synthetic’ code mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both Supervised and Unsupervised NMT.
 This paper proposes a new method to generate parallel sentences combining NMT and GANs. While I think that the paper contains many interesting ideas, it lacks a good motivation and, more importantly, I find the experimental design (as well as some results) to be very weak. Please find my comments below: - The paper is not properly motivated. Why would one want to generate parallel sentences? I do not mean that the problem is uninteresting, I am just saying that the motivation is not there. My initial expectation (before I read the paper) was that the NMT part would benefit (or at least be influenced by) the text generation part, but that is not the case, as the NMT model is trained offline before the generator. - You do not compare your results to any other system or baseline in your experiments. A simple baseline would be to generate sentences monolingually, and translate them using machine translation (either supervised or unsupervised). I think that some baseline like this is necessary. - The translation BLEU scores are very weak. 8 BLEU points in fr-en Europarl seems way too low to take these results seriously. - Given that the (unsupervised) machine translation part is completely independent from the text generation unit, its evaluation seems of little relevance, as there is nothing new on it. - The training set seems very small for natural language generation (200k sentences for Europarl and 12-29k for Multi30k), so I am not sure about how meaningful the reported results are. - I do not understand what the "generation BLEU" is, but evaluating a text generator with BLEU does not seem to make much sense, as there is no reference to compare to. - I am not sure if I understand how perplexity is used in your evaluation. Do you train a separate language model and use it to measure the perplexity of your generated text? If so, this seems unusual and problematic to me. Do you have any reference of anybody else doing this?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Bilingual-GAN: Neural Text Generation and Neural Machine Translation as Two Sides of the Same Coin
Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive results in text generation and Unsupervised NMT respectively. Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is adversarially constrained to be shared between both languages. First an NMT model is trained, with back-translation and an adversarial setup, to enforce a latent state between the two languages. The encoder and decoder are shared for the two translation directions. Next, a GAN is trained to generate ‘synthetic’ code mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both Supervised and Unsupervised NMT.
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 a new method to generate parallel sentences combining NMT and GANs. While I think that the paper contains many interesting ideas, it lacks a good motivation and, more importantly, I find the experimental design (as well as some results) to be very weak. Please find my comments below: - The paper is not properly motivated. Why would one want to generate parallel sentences? I do not mean that the problem is uninteresting, I am just saying that the motivation is not there. My initial expectation (before I read the paper) was that the NMT part would benefit (or at least be influenced by) the text generation part, but that is not the case, as the NMT model is trained offline before the generator. - You do not compare your results to any other system or baseline in your experiments. A simple baseline would be to generate sentences monolingually, and translate them using machine translation (either supervised or unsupervised). I think that some baseline like this is necessary. - The translation BLEU scores are very weak. 8 BLEU points in fr-en Europarl seems way too low to take these results seriously. - Given that the (unsupervised) machine translation part is completely independent from the text generation unit, its evaluation seems of little relevance, as there is nothing new on it. - The training set seems very small for natural language generation (200k sentences for Europarl and 12-29k for Multi30k), so I am not sure about how meaningful the reported results are. - I do not understand what the "generation BLEU" is, but evaluating a text generator with BLEU does not seem to make much sense, as there is no reference to compare to. - I am not sure if I understand how perplexity is used in your evaluation. Do you train a separate language model and use it to measure the perplexity of your generated text? If so, this seems unusual and problematic to me. Do you have any reference of anybody else doing this?
assistant
 This paper proposes a new method to generate parallel sentences combining NMT and GANs. While I think that the paper contains many interesting ideas, it lacks a good motivation and, more importantly, I find the experimental design (as well as some results) to be very weak. Please find my comments below: - The paper is not properly motivated. Why would one want to generate parallel sentences? I do not mean that the problem is uninteresting, I am just saying that the motivation is not there. My initial expectation (before I read the paper) was that the NMT part would benefit (or at least be influenced by) the text generation part, but that is not the case, as the NMT model is trained offline before the generator. - You do not compare your results to any other system or baseline in your experiments. A simple baseline would be to generate sentences monolingually, and translate them using machine translation (either supervised or unsupervised). I think that some baseline like this is necessary. - The translation BLEU scores are very weak. 8 BLEU points in fr-en Europarl seems way too low to take these results seriously. - Given that the (unsupervised) machine translation part is completely independent from the text generation unit, its evaluation seems of little relevance, as there is nothing new on it. - The training set seems very small for natural language generation (200k sentences for Europarl and 12-29k for Multi30k), so I am not sure about how meaningful the reported results are. - I do not understand what the "generation BLEU" is, but evaluating a text generator with BLEU does not seem to make much sense, as there is no reference to compare to. - I am not sure if I understand how perplexity is used in your evaluation. Do you train a separate language model and use it to measure the perplexity of your generated text? If so, this seems unusual and problematic to me. Do you have any reference of anybody else doing this?