Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However, to achieve this, contemporary NMNs need strong supervision in executing the query as a specialized program over reasoning modules and fail to generalize to more open-ended settings without such supervision. Hence we propose Weakly-Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. It learns to execute a noisy heuristic program obtained from the dependency parsing of the query, as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the numerical-answer subset of DROP, WNSMN out-performs NMN by 32% and the reasoning-free language model GenBERT by 8% in exact match accuracy when trained under comparable weak supervised settings. This showcases the effectiveness and generalizability of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
Bosch Research（博世研究院）；Carnegie Mellon University（卡内基梅隆大学）；Information Sciences Institute, Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院信息科学研究所）；Michigan State University（密歇根州立大学）
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
University of Macau（澳门大学）；University of Chinese Academy of Sciences（中国科学院大学）；The Australian National University（澳大利亚国立大学）；Juntendo University（静冈大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Central South University（中南大学）；Southeast University（东南大学）；China Agricultural University（中国农业大学）；Adelaide University（阿德莱德大学）
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
Texas A&M University（德克萨斯农工大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Maryland（马里兰大学）；University of California, Riverside（加利福尼亚大学河滨分校）；University of Pittsburgh（匹兹堡大学）
Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence
TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Eindhoven University of Technology (TU/e)（埃因霍温理工大学）；University of Warwick（华威大学）；Leibniz Institute for Solid State and Materials Research (IFW)（莱布尼茨固体与材料研究所）
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；IIIS, Tsinghua University（清华大学交叉信息研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Deepmind（深度思考）；MIT BCS（麻省理工学院脑与认知科学系）；CBMM（脑、心智与机器中心）
Protect$^*$: Steerable Retrosynthesis through Neuro-Symbolic State Encoding
Deep Forest Sciences（深林科技）；Departament de Farmacologia, Toxicologia i Química Terapèutica, Universitat de Barcelona（巴塞罗那大学药理学、毒理学与治疗化学系）；Institut de Nanociència i Nanotecnologia IN2UB, Universitat de Barcelona（巴塞罗那大学纳米科学与纳米技术研究所）
