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.
 This paper propose an extension of the Neural Theorem Provers (NTP) system that addresses the main issues of this method. The contributions of this paper allow to use this model on real-word datasets by reducing the time and space complexity of the NTP model. Pro: The paper is clear and well written and the contribution is relevant to ICLR. NTP systems by combining the advantages of neural models and symbolic reasoning are a promising research direction. Even though the results presented are lower than previous studies, they present the advantage of being interpretable. Cons: I*m not convinced by the model used to integrate textual mentions. The evaluation proposed in section 6.3 proposes to replace training triples by textual mention in order to evaluate the encoding module. However, it seems to me that, in this particular case, these mentions are very short sentences. This could explained why such a simplistic model that simply average word embeddings is sufficient. I wonder if this would still work for more realistic (and thus longer) sentences. Minor issues: -Page 1: In particular [...] (NLU) and [...] (MR) in particular, ...
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### 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.
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<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 propose an extension of the Neural Theorem Provers (NTP) system that addresses the main issues of this method. The contributions of this paper allow to use this model on real-word datasets by reducing the time and space complexity of the NTP model. Pro: The paper is clear and well written and the contribution is relevant to ICLR. NTP systems by combining the advantages of neural models and symbolic reasoning are a promising research direction. Even though the results presented are lower than previous studies, they present the advantage of being interpretable. Cons: I*m not convinced by the model used to integrate textual mentions. The evaluation proposed in section 6.3 proposes to replace training triples by textual mention in order to evaluate the encoding module. However, it seems to me that, in this particular case, these mentions are very short sentences. This could explained why such a simplistic model that simply average word embeddings is sufficient. I wonder if this would still work for more realistic (and thus longer) sentences. Minor issues: -Page 1: In particular [...] (NLU) and [...] (MR) in particular, ...
assistant
 This paper propose an extension of the Neural Theorem Provers (NTP) system that addresses the main issues of this method. The contributions of this paper allow to use this model on real-word datasets by reducing the time and space complexity of the NTP model. Pro: The paper is clear and well written and the contribution is relevant to ICLR. NTP systems by combining the advantages of neural models and symbolic reasoning are a promising research direction. Even though the results presented are lower than previous studies, they present the advantage of being interpretable. Cons: I*m not convinced by the model used to integrate textual mentions. The evaluation proposed in section 6.3 proposes to replace training triples by textual mention in order to evaluate the encoding module. However, it seems to me that, in this particular case, these mentions are very short sentences. This could explained why such a simplistic model that simply average word embeddings is sufficient. I wonder if this would still work for more realistic (and thus longer) sentences. Minor issues: -Page 1: In particular [...] (NLU) and [...] (MR) in particular, ...