Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: In this paper, the authors study the web navigation problem. They propose behavioral cloning of the random trajectory method on Wikipedia graphs. They further apply their method to the FEVER verification task. Their experiments show that their method is effective in learning entity embeddings and a navigation policy. It also succeeds in a competitive performance in the verification task. 
strengths_and_weaknesses: Strengths:
1) The proposed method is more effective than the baseline methods and scalable to large Wikipedia graphs. 
2) The extensive analysis of results and limitations of the study are presented.

Weaknesses:
Major:
1)  The proposed trajectories are very intuitive and lack novelty. They simply use random walks with the probabilities being decided based on neighboring vertices.  
2) The proposed method is only compared with random and greedy methods, not other state-of-the-art methods. Hence, it is difficult to measure effectiveness.
3) A comprehensive efficiency analysis would be helpful. Comparison between the running times of the proposed methods and other methods would give a better understanding of the method.

Minor: 
1) There are some typing issues. For example, on page 8, line 301, "Table 2" should be "Table 4". 

questions: Please refer to the major weaknesses mentioned above. 
limitations: Yes, they mentioned two limitations of their work.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper focuses on the problem of web navigation. The authors proposed to train an agent which can navigate a large Wikipedia graph. They first generate sampled trajectories on the Wikipedia graph based on different methods (e.g. random forward trajectories, reverse trajectories, shortest paths). With these sampled trajectories, the behavioral cloning (i.e. supervised learning) method is adopted to train the model. The experiments show that the proposed method is not only able to efficiently navigate Wikipedia graph but also have competitive performance on the fact verification task.
strengths_and_weaknesses: (1) Strengths
* The proposed method works well on navigation task and obtains competitive performance on the fact verification task.
* This paper is generally well written.

(2) Weaknesses
* Nogueira and Cho (2016)  is the most relevant work. The proposed method WebNav in Nogueira and Cho (2016) only gets 12.5% success rate, while the proposed method in this paper obtains more than 90% success rate in a similar setting. The reason for this large difference of performance is not explained or discussed in the experiments.

questions: * If RL is adopted after behavioral cloning (i.e. RFBC+RL in Table 2), what's the performance?
* What are the training details, e.g. hardware, and training time?
* line 301, table 2 -> table 4?
limitations: The authors have discussed the limitation of this work.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper presents a web navigation agent that learns to reach target webpages by hopping hyperlinks. The paper uses Transformer-based models that encode the local content and learn to select the link the agent moves to next. This model is trained by behavior cloning of randomly sampled trajectories. The authors show that this approach provides improved performance on navigation and fact verification benchmarks.
strengths_and_weaknesses: Strengths
- The proposed system is clean, and performs better than existing systems on navigation and fact verification benchmarks 
- Good execution - the paper conducts various ablation studies such as different navigation setups and different design choices of the method (trajectory sampling, policy features, etc.) 

Weaknesses
- This might be due to the presentation of the paper, but the technical novelty or contribution appears somewhat limited to me after reading the paper. The method seems to be a fairly simple application of existing techniques such as Transformer encoder and behavioral cloning training. The experiments also seem not quite offering significant insights or takeaways for the Neurips community. It would be really helpful if the authors could crisply clarify the main contribution with respect to existing works, e.g. is it a new methodology, or a new empirical finding (if so, what might be the impact to the research community), or an improved performance (if so, what exact difference from prior works is enabling the improved performance)?
- The navigation experiments are only conducted on a Wikipedia graph, which is smaller than the real-world web
questions: - I wonder if the authors have insights into why RL performs more poorly than the proposed RFBC (Table 2)?
- Suggestion for related work discussion: there is a larger body of existing works in machine learning for Wikipedia hyperlink graphs, which may be worth discussing -- e.g. https://arxiv.org/abs/2203.15827 (Transformer language model that predicts hyperlinked pages from the current page), https://arxiv.org/abs/1911.10470 (QA model that predicts hyperlinked pages). The main difference of the proposed work from these papers seems that it focuses on longer-horizon navigation with a target in consideration.

limitations: - The authors discusses the limitation in the paper.
review_title: null

Meta-review: metareview: I concur with the decision of the meta reviewer to accept the paper. While the technical novelty may be limited, the excellent empirical performance of the overall system of many components is an interesting point to share with the community.
confidence: Certain
Decision: Accept