Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces "attended attention" for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-ofthe-art systems by a large margin in public datasets, such as CNN and Children's Book Test.
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pretrained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. 1
Owing to the continuous contributions by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available, and they have been pushing Chinese MRC research forward. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We hope the release of the dataset could further accelerate the machine reading comprehension research. 1
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
This article has an accompanying continuing medical education activity, also eligible for MOC credit, on page e135. Learning Objective-Upon completion of this activity, successful learners will be able to list the criteria for diagnosis of cirrhosis with acute decompensation; list at least 1 independent risk factor for portal vein thrombosis in patients with cirrhosis and acute decompensation; list 3 indicators for acute decompensation in patients with cirrhosis; and know the impact of portal vein thrombosis on 1-year mortality in patients with cirrhosis and acute decompensation. BACKGROUND & AIMS: Portal vein thrombosis (PVT) is a common and serious complication in patients with cirrhosis. However, little is known about PVT in patients with cirrhosis and acute decompensation (AD). We investigated the prevalence and clinical significance of PVT in nonmalignant patients with cirrhosis and AD. METHODS: We performed a retrospective study of 2 cohorts of patients with acute exacerbation of chronic liver disease who participated in the Chinese AcuTe on CHronic LIver FailurE study, established by
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.