Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chat-bots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chat-bot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous reinforcement learning work for natural language uses on-policy updates and/or is designed for on-line learning settings. We demonstrate empirically that such strategies are not appropriate for this setting and develop an off-policy batch policy gradient method (_x0008_pg). We demonstrate the efficacy of our method via a series of synthetic experiments and an Amazon Mechanical Turk experiment on a restaurant recommendations dataset.
The author propose to use a off-policy actor-critic algorithm in a batch-setting to improve chat-bots. The approach is well motivated and the paper is well written, except for some intuitions for why the batch version outperforms the on-line version (see comments on *clarification regarding batch vs. online setting*). The artificial experiments are instructive, and the real-world experiments were performed very thoroughly although the results show only modest improvement.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chat-bots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chat-bot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous reinforcement learning work for natural language uses on-policy updates and/or is designed for on-line learning settings. We demonstrate empirically that such strategies are not appropriate for this setting and develop an off-policy batch policy gradient method (_x0008_pg). We demonstrate the efficacy of our method via a series of synthetic experiments and an Amazon Mechanical Turk experiment on a restaurant recommendations dataset.
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 author propose to use a off-policy actor-critic algorithm in a batch-setting to improve chat-bots. The approach is well motivated and the paper is well written, except for some intuitions for why the batch version outperforms the on-line version (see comments on *clarification regarding batch vs. online setting*). The artificial experiments are instructive, and the real-world experiments were performed very thoroughly although the results show only modest improvement.
assistant
The author propose to use a off-policy actor-critic algorithm in a batch-setting to improve chat-bots. The approach is well motivated and the paper is well written, except for some intuitions for why the batch version outperforms the on-line version (see comments on *clarification regarding batch vs. online setting*). The artificial experiments are instructive, and the real-world experiments were performed very thoroughly although the results show only modest improvement.