Commonsense question answering(CQA)requires understanding and reasoning over QA context and related commonsense knowledge,such as a structured Knowledge Graph(KG).Existing studies combine language models and graph neu...Commonsense question answering(CQA)requires understanding and reasoning over QA context and related commonsense knowledge,such as a structured Knowledge Graph(KG).Existing studies combine language models and graph neural networks to model inference.However,traditional knowledge graph are mostly concept-based,ignoring direct path evidence necessary for accurate reasoning.In this paper,we propose MRGNN(Meta-path Reasoning Graph Neural Network),a novel model that comprehensively captures sequential semantic information from concepts and paths.In MRGNN,meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously.We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets,showing the effectiveness of MRGNN.Also,we conduct further ablation experiments and explain the reasoning behavior through the case study.展开更多
The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-pheno...The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals.However,the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels(i.e.,sparse,low sparse,non-sparse).Actually,the real association patterns related to different host phenotypes are not unique.Here,we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels,designated as MiATDS.In particular,we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information.We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method.We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power.By applying to real data analysis,MiATDS displays reliable practicability too.The R package is available at https://github.com/XiaoyunHuang33/MiATDS.展开更多
1 Introduction.Inspired by the impressive success of BERT[1]in various NLP applications,researchers have attempted to apply pretrained language models to information retrieval,and existing BERT-based retrieval models ...1 Introduction.Inspired by the impressive success of BERT[1]in various NLP applications,researchers have attempted to apply pretrained language models to information retrieval,and existing BERT-based retrieval models obtain improved performance on passage retrieval[2-4].Since BERT has the limitation that the maximum length of tokens is only 512,however,simply applying those models to the task of long document retrieval derives suboptimal results.展开更多
基金supported by the Key Research and Development Program of Hubei Province(2020BAB017)the Scientific Research Center Program of National Language Commission(ZDI135-135)the Fundamental Research Funds for the Central Universities(KJ02502022-0155,CCNU22XJ037).
文摘Commonsense question answering(CQA)requires understanding and reasoning over QA context and related commonsense knowledge,such as a structured Knowledge Graph(KG).Existing studies combine language models and graph neural networks to model inference.However,traditional knowledge graph are mostly concept-based,ignoring direct path evidence necessary for accurate reasoning.In this paper,we propose MRGNN(Meta-path Reasoning Graph Neural Network),a novel model that comprehensively captures sequential semantic information from concepts and paths.In MRGNN,meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously.We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets,showing the effectiveness of MRGNN.Also,we conduct further ablation experiments and explain the reasoning behavior through the case study.
基金supported by the National Natural Science Foundation of China(61872157,61932008,61532008)the Key Research and Development Program of Hubei Province(2020BAB017)。
文摘The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals.However,the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels(i.e.,sparse,low sparse,non-sparse).Actually,the real association patterns related to different host phenotypes are not unique.Here,we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels,designated as MiATDS.In particular,we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information.We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method.We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power.By applying to real data analysis,MiATDS displays reliable practicability too.The R package is available at https://github.com/XiaoyunHuang33/MiATDS.
基金supported by the Key Research and Development Program of Hubei Province(2020BAB017)Scientific Research Center Program of National Language Commission(ZDI135-135)the Fundamental Research Funds for the Central Universities(CCNU22QN015).
文摘1 Introduction.Inspired by the impressive success of BERT[1]in various NLP applications,researchers have attempted to apply pretrained language models to information retrieval,and existing BERT-based retrieval models obtain improved performance on passage retrieval[2-4].Since BERT has the limitation that the maximum length of tokens is only 512,however,simply applying those models to the task of long document retrieval derives suboptimal results.
