Scalable Neural Theorem Proving on Knowledge Bases and Natural Language
Reasoning over text and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. Transducing text to logical forms which can be operated on is a brittle and error-prone process. Operating directly on text by jointly learning representations and transformations thereof by means of neural architectures that lack the ability to learn and exploit general rules can be very data-inefficient and not generalise correctly. These issues are addressed by Neural Theorem Provers (NTPs) (Rocktäschel & Riedel, 2017), neuro-symbolic systems based on a continuous relaxation of Prolog’s backward chaining algorithm, where symbolic unification between atoms is replaced by a differentiable operator computing the similarity between their embedding representations. In this paper, we first propose Neighbourhood-approximated Neural Theorem Provers (NaNTPs) consisting of two extensions toNTPs, namely a) a method for drastically reducing the previously prohibitive time and space complexity during inference and learning, and b) an attention mechanism for improving the rule learning process, deeming them usable on real-world datasets. Then, we propose a novel approach for jointly reasoning over KB facts and textual mentions, by jointly embedding them in a shared embedding space. The proposed method is able to extract rules and provide explanations—involving both textual patterns and KB relations—from large KBs and text corpora. We show that NaNTPs perform on par with NTPs at a fraction of a cost, and can achieve competitive link prediction results on challenging large-scale datasets, including WN18, WN18RR, and FB15k-237 (with and without textual mentions) while being able to provide explanations for each prediction and extract interpretable rules.
 The authors propose several techniques to speed up the previously proposed Neural Theorem Prover approach. The techniques are evaluated via empirical results on several benchmark datasets. Learning interpretable models is an important topic and the results here are interesting and valuable to the community. However, I feel that the paper in its current form is not yet ready for publication in ICLR, for the following reasons: 1) The authors propose three improvements. The first is a speed-up through nearest neighbor search instead of a brute-force search. This is the most elaborated section out of the three, yet seems like the most trivial -- unless the authors can provide an analytical bound on the loss in ntp score w.r.t the neighborhood size. It is a standard and well-known technique to restrict the search to a neighborhood, widely used in any applications of word embedding (e.g. in Khot et el*s Markov Logic Networks for Natural Language Question Answering). The attention mechanism (essentially reducing the model capacity) is also well-known but its effect in this particular framework is not properly elaborated. The same can be said for the use of mentions. 2) The section on experiment results seems a bit rushed -- the authors did mention some last-minute discovery that may affect some of the presented results. The section can be a little hard to parse. In particular, it would be useful for the authors to focus on providing more insights on how the proposed techniques improve the results, and in what ways. 3) Section 2 on the NTP framework is not very helpful for a reader that has not read the previous paper on NTP (in particular, the part on training and rule learning). For a reader that has done so, the section feels redundant.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Scalable Neural Theorem Proving on Knowledge Bases and Natural Language
Reasoning over text and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. Transducing text to logical forms which can be operated on is a brittle and error-prone process. Operating directly on text by jointly learning representations and transformations thereof by means of neural architectures that lack the ability to learn and exploit general rules can be very data-inefficient and not generalise correctly. These issues are addressed by Neural Theorem Provers (NTPs) (Rocktäschel & Riedel, 2017), neuro-symbolic systems based on a continuous relaxation of Prolog’s backward chaining algorithm, where symbolic unification between atoms is replaced by a differentiable operator computing the similarity between their embedding representations. In this paper, we first propose Neighbourhood-approximated Neural Theorem Provers (NaNTPs) consisting of two extensions toNTPs, namely a) a method for drastically reducing the previously prohibitive time and space complexity during inference and learning, and b) an attention mechanism for improving the rule learning process, deeming them usable on real-world datasets. Then, we propose a novel approach for jointly reasoning over KB facts and textual mentions, by jointly embedding them in a shared embedding space. The proposed method is able to extract rules and provide explanations—involving both textual patterns and KB relations—from large KBs and text corpora. We show that NaNTPs perform on par with NTPs at a fraction of a cost, and can achieve competitive link prediction results on challenging large-scale datasets, including WN18, WN18RR, and FB15k-237 (with and without textual mentions) while being able to provide explanations for each prediction and extract interpretable rules.
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 several techniques to speed up the previously proposed Neural Theorem Prover approach. The techniques are evaluated via empirical results on several benchmark datasets. Learning interpretable models is an important topic and the results here are interesting and valuable to the community. However, I feel that the paper in its current form is not yet ready for publication in ICLR, for the following reasons: 1) The authors propose three improvements. The first is a speed-up through nearest neighbor search instead of a brute-force search. This is the most elaborated section out of the three, yet seems like the most trivial -- unless the authors can provide an analytical bound on the loss in ntp score w.r.t the neighborhood size. It is a standard and well-known technique to restrict the search to a neighborhood, widely used in any applications of word embedding (e.g. in Khot et el*s Markov Logic Networks for Natural Language Question Answering). The attention mechanism (essentially reducing the model capacity) is also well-known but its effect in this particular framework is not properly elaborated. The same can be said for the use of mentions. 2) The section on experiment results seems a bit rushed -- the authors did mention some last-minute discovery that may affect some of the presented results. The section can be a little hard to parse. In particular, it would be useful for the authors to focus on providing more insights on how the proposed techniques improve the results, and in what ways. 3) Section 2 on the NTP framework is not very helpful for a reader that has not read the previous paper on NTP (in particular, the part on training and rule learning). For a reader that has done so, the section feels redundant.
assistant
 The authors propose several techniques to speed up the previously proposed Neural Theorem Prover approach. The techniques are evaluated via empirical results on several benchmark datasets. Learning interpretable models is an important topic and the results here are interesting and valuable to the community. However, I feel that the paper in its current form is not yet ready for publication in ICLR, for the following reasons: 1) The authors propose three improvements. The first is a speed-up through nearest neighbor search instead of a brute-force search. This is the most elaborated section out of the three, yet seems like the most trivial -- unless the authors can provide an analytical bound on the loss in ntp score w.r.t the neighborhood size. It is a standard and well-known technique to restrict the search to a neighborhood, widely used in any applications of word embedding (e.g. in Khot et el*s Markov Logic Networks for Natural Language Question Answering). The attention mechanism (essentially reducing the model capacity) is also well-known but its effect in this particular framework is not properly elaborated. The same can be said for the use of mentions. 2) The section on experiment results seems a bit rushed -- the authors did mention some last-minute discovery that may affect some of the presented results. The section can be a little hard to parse. In particular, it would be useful for the authors to focus on providing more insights on how the proposed techniques improve the results, and in what ways. 3) Section 2 on the NTP framework is not very helpful for a reader that has not read the previous paper on NTP (in particular, the part on training and rule learning). For a reader that has done so, the section feels redundant.