Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and ...Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly.However,prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge.The low-resource training data further increase the challenge for the data-driven large models in this task.To address those challenges,we propose a collaborative knowledge infusion approach for low-resource stance detection tasks,employing a combination of aligned knowledge enhancement and efficient parameter learning techniques.Specifically,our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment.Additionally,we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm,which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives.To assess the effectiveness of our method,we conduct extensive experiments on three public stance detection datasets,including low-resource and cross-target settings.The results demonstrate significant performance improvements compared to the existing stance detection approaches.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
基金supported by the RCA founding of A*STAR and DSO National Laboratory(Nos.2208-526-RCA-CFAR and SC23/22-3204FA)。
文摘Stance detection is the view towards a specific target by a given context(e.g.tweets,commercial reviews).Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly.However,prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge.The low-resource training data further increase the challenge for the data-driven large models in this task.To address those challenges,we propose a collaborative knowledge infusion approach for low-resource stance detection tasks,employing a combination of aligned knowledge enhancement and efficient parameter learning techniques.Specifically,our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment.Additionally,we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm,which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives.To assess the effectiveness of our method,we conduct extensive experiments on three public stance detection datasets,including low-resource and cross-target settings.The results demonstrate significant performance improvements compared to the existing stance detection approaches.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
