164 research outputs found
A Study of The Impact of Financial Development on the Country’s Monetization
Based on dynamic panel data from 45 countries, this article makes an empirical analysis of the determinants of M2/GDP ratio. It reveals that indirect financing dominated by banking system and direct financing dominated by financial markets jointly contribute to the rise of the M2/GDP ratio of a country, while the improvement of efficiency of banking industry and securities market helps reduce it. Finally it offers some suggestions on upgrading China’s financial market and structure in terms of promoting its financial efficiency, innovation and reform
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently
and has attracted a lot of attention. However, the existing reading
comprehension datasets are mostly in English. In this paper, we introduce a
Span-Extraction dataset for Chinese machine reading comprehension to add
language diversities in this area. The dataset is composed by near 20,000 real
questions annotated on Wikipedia paragraphs by human experts. We also annotated
a challenge set which contains the questions that need comprehensive
understanding and multi-sentence inference throughout the context. We present
several baseline systems as well as anonymous submissions for demonstrating the
difficulties in this dataset. With the release of the dataset, we hosted the
Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC
2018). We hope the release of the dataset could further accelerate the Chinese
machine reading comprehension research. Resources are available:
https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short
paper
Nonlinear stability for 3-D plane Poiseuille flow in a finite channel
In this paper, we study the nonlinear stability for the 3-D plane Poiseuille
flow at high Reynolds number in a finite channel
with non-slip boundary condition.
We prove that if the initial velocity satisfies
for some
independent of , then the solution of 3-D Naiver-Stokes equations
is global in time and does not transit away from the plane Poiseuille flow. To
our knowledge, this is the first nonlinear stability result for the 3-D plane
Poiseuille flow and the transition threshold is accordant with the numerical
result by Lundbladh et al. \cite{LHR}
Neural Cognitive Diagnosis for Intelligent Education Systems
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
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
In this paper, we introduce TextBrewer, an open-source knowledge distillation
toolkit designed for natural language processing. It works with different
neural network models and supports various kinds of supervised learning tasks,
such as text classification, reading comprehension, sequence labeling.
TextBrewer provides a simple and uniform workflow that enables quick setting up
of distillation experiments with highly flexible configurations. It offers a
set of predefined distillation methods and can be extended with custom code. As
a case study, we use TextBrewer to distill BERT on several typical NLP tasks.
With simple configurations, we achieve results that are comparable with or even
higher than the public distilled BERT models with similar numbers of
parameters. Our toolkit is available through: http://textbrewer.hfl-rc.comComment: To appear at ACL 2020 Demo Sessio
Generative Input: Towards Next-Generation Input Methods Paradigm
Since the release of ChatGPT, generative models have achieved tremendous
success and become the de facto approach for various NLP tasks. However, its
application in the field of input methods remains under-explored. Many neural
network approaches have been applied to the construction of Chinese input
method engines(IMEs).Previous research often assumed that the input pinyin was
correct and focused on Pinyin-to-character(P2C) task, which significantly falls
short of meeting users' demands. Moreover, previous research could not leverage
user feedback to optimize the model and provide personalized results. In this
study, we propose a novel Generative Input paradigm named GeneInput. It uses
prompts to handle all input scenarios and other intelligent auxiliary input
functions, optimizing the model with user feedback to deliver personalized
results. The results demonstrate that we have achieved state-of-the-art
performance for the first time in the Full-mode Key-sequence to
Characters(FK2C) task. We propose a novel reward model training method that
eliminates the need for additional manual annotations and the performance
surpasses GPT-4 in tasks involving intelligent association and conversational
assistance. Compared to traditional paradigms, GeneInput not only demonstrates
superior performance but also exhibits enhanced robustness, scalability, and
online learning capabilities
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