Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202

Extracting event temporal relations via hyperbolic geometry

Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces

Event-centric question answering via contrastive learning and invertible event transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR

A Novel Iterative and Dynamic Trust Computing Model for Large Scaled P2P Networks

Trust management has been emerging as an essential complementary part to security mechanisms of P2P systems, and trustworthiness is one of the most important concepts driving decision making and establishing reliable relationships. Collusion attack is a main challenge to distributed P2P trust model. Large scaled P2P systems have typical features, such as large scaled data with rapid speed, and this paper presented an iterative and dynamic trust computation model named IDTrust (Iterative and Dynamic Trust model) according to these properties. First of all, a three-layered distributed trust communication architecture was presented in IDTrust so as to separate evidence collector and trust decision from P2P service. Then an iterative and dynamic trust computation method was presented to improve efficiency, where only latest evidences were enrolled during one iterative computation. On the basis of these, direct trust model, indirect trust model, and global trust model were presented with both explicit and implicit evidences. We consider multifactors in IDTrust model according to different malicious behaviors, such as similarity, successful transaction rate, and time decay factors. Simulations and analysis proved the rightness and efficiency of IDTrust against attacks with quick respond and sensitiveness during trust decision

Event temporal relation extraction with Bayesian translational model

Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. Compared to conventional neural approaches, instead of performing point estimation to find the best set parameters, the proposed model infers the parameters' posterior distribution directly, enhancing the model's capability to encode and express uncertainty about the predictions. Experimental results on the three widely used datasets show that Bayesian-Trans outperforms existing approaches for event temporal relation extraction. We additionally present detailed analyses on uncertainty quantification, comparison of priors, and ablation studies, illustrating the benefits of the proposed approach

Learning refined features for open-world text classification with class description and commonsense knowledge

Open-world classification requires a classifier not only to classify samples of the observed classes but also to detect samples which are not suitable to be classified as the known classes. State-of-the-art methods train a feature extractor to extract features for separating known classes with limited training data. Then some strategies, such as outlier detector, are used to reject samples from unknown classes based on the feature space. However, they are prone to extract the discriminative features among known classes and cannot model comprehensive features of known classes, which causes the classification errors when detecting the samples from the unknown classes in an open world scenario. Motivated by the theory of psychology and cognitive science, we utilize both class descriptions and commonsense knowledge summarized by human to refine the discriminant features and propose a regularization strategy. The regularization is incorporated into the feature extractor, which is enabled to further improve the performance of our model in an open-world environment. Extensive experiments and visualization analysis are conducted to evaluate the effectiveness of our proposed model

Learning refined features for open-world text classification

Open-world classification requires a classifier not only to classify samples of the observed classes but also to detect samples which are not suitable to be classified as the known classes. State-of-the-art methods train a network to extract features for separating known classes firstly. Then some strategies, such as outlier detector, are used to reject samples from unknown classes based on the feature space. However, this network as a feature extractor cannot model comprehensive features of known classes in an open world scenario due to limited training data. This causes a problem that the strategies are unable to separate unknown classes from known classes accurately in this feature space. Motivated by the theory of psychology and cognitive science, we utilize class descriptions summarized by human to refine discriminant features and propose a regularization with class descriptions. The regularization is incorporated into DOC (one of state-of-the-art models) to improve the performance of open-world classification. The experiments on two text classification datasets demonstrate the effectiveness of the proposed method

Association between the surgical approach and prognosis of spontaneous supratentorial deep intracerebral hemorrhage

Abstract The association between surgical approach and prognosis in patients with spontaneous supratentorial deep intracerebral hemorrhage is unclear. We aimed to explore the association between surgical approach and prognosis in these patients. A retrospective cohort of 311 patients from 3 centers who were treated with surgery 24 h after ictus was recruited. The surgical procedure involved removing the intracerebral hematoma using an aspirator through either the cortical approach or Sylvian fissure approach, assisted by an endoscope or microscope. The primary outcome was the one-year modified Rankin scale (mRS) score. The association between the surgical approach and the one-year mRS score was explored by using ordinal logistic regression and binary logistic regression. Baseline characteristics were balanced by propensity score matching and inverse propensity score weighting. In the adjusted analysis, compared with the cortex approach group, the Sylvian fissure approach group had better one-year mRS scores when analyzed as an ordinal variable (3.00 [2.00–4.00] vs. 4.00 [3.00–5.00]; adjusted odds ratio, 3.15; 95% CI, 1.78–5.58; p  0.999) or three-month mortality (Fisher's exact test, p > 0.999). Inverse probability weighted regression analysis showed better one-year mRS scores when analyzed as an ordinal variable (adjusted odds ratio, 3.03; 95% CI, 2.17–4.17; p < 0.001) and a dichotomous variable (adjusted odds ratio, 3.11; 95% CI, 2.16–4.77; p < 0.001) in the Sylvian fissure approach group; the surgical approach was not significantly associated with rebleeding (p = 0.50) or three-month mortality (p = 0.60). In the surgical treatment of patients with spontaneous supratentorial deep intracerebral hemorrhage, the Sylvian fissure approach may lead to a better functional outcome compared with the cortex approach. Future prospective studies are warranted to confirm this finding