Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation
Question generation is an important task for improving our ability to process natural language data, with additional challenges over other sequence transformation tasks. Recent approaches use modifications to a Seq2Seq architecture inspired by advances in machine translation, but unlike translation the input and output vocabularies overlap significantly, and there are many different valid questions for each input. Approaches using copy mechanisms and reinforcement learning have shown promising results, but there are ambiguities in the exact implementation that have not yet been investigated. We show that by removing inductive bias from the model and allowing the choice of generation path to become latent, we achieve substantial improvements over implementations biased with both naive and smart heuristics. We perform a human evaluation to confirm these findings. We show that although policy gradient methods may be used to decouple training from the ground truth and optimise directly for quality metrics that have previously been assumed to be good choices, these objectives are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source. Finally, we show that an adversarial objective learned directly from the ground truth data is not able to generate a useful training signal.
 This paper presents question generation models by designing variations of copying mechanism and reward functions. Experimental results show that different copying mechanism can improve upon basic seq2seq models, some of the reward functions also produce better results. I think the results are interesting, especially the ones compared with human evaluation (fig. 1), but it*s might be better to explain on which aspect each of the feature contributes to the improvement. For instance, the authors can give some insights based on empirical results on what kind of questions will benefit from each type of copying. The authors should better organize table 1 and 2, and inform the readers on what is the consistent conclusion (if any). For table 2, there is no result for adding "adversarial discriminator" only. Also the item "+226" on the second row, is that an error?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation
Question generation is an important task for improving our ability to process natural language data, with additional challenges over other sequence transformation tasks. Recent approaches use modifications to a Seq2Seq architecture inspired by advances in machine translation, but unlike translation the input and output vocabularies overlap significantly, and there are many different valid questions for each input. Approaches using copy mechanisms and reinforcement learning have shown promising results, but there are ambiguities in the exact implementation that have not yet been investigated. We show that by removing inductive bias from the model and allowing the choice of generation path to become latent, we achieve substantial improvements over implementations biased with both naive and smart heuristics. We perform a human evaluation to confirm these findings. We show that although policy gradient methods may be used to decouple training from the ground truth and optimise directly for quality metrics that have previously been assumed to be good choices, these objectives are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source. Finally, we show that an adversarial objective learned directly from the ground truth data is not able to generate a useful training signal.
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 presents question generation models by designing variations of copying mechanism and reward functions. Experimental results show that different copying mechanism can improve upon basic seq2seq models, some of the reward functions also produce better results. I think the results are interesting, especially the ones compared with human evaluation (fig. 1), but it*s might be better to explain on which aspect each of the feature contributes to the improvement. For instance, the authors can give some insights based on empirical results on what kind of questions will benefit from each type of copying. The authors should better organize table 1 and 2, and inform the readers on what is the consistent conclusion (if any). For table 2, there is no result for adding "adversarial discriminator" only. Also the item "+226" on the second row, is that an error?
assistant
 This paper presents question generation models by designing variations of copying mechanism and reward functions. Experimental results show that different copying mechanism can improve upon basic seq2seq models, some of the reward functions also produce better results. I think the results are interesting, especially the ones compared with human evaluation (fig. 1), but it*s might be better to explain on which aspect each of the feature contributes to the improvement. For instance, the authors can give some insights based on empirical results on what kind of questions will benefit from each type of copying. The authors should better organize table 1 and 2, and inform the readers on what is the consistent conclusion (if any). For table 2, there is no result for adding "adversarial discriminator" only. Also the item "+226" on the second row, is that an error?