Word Embedding based Correlation Model for Question/Answer Matching

With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.Comment: 8 pages, 2 figure

Task-technology Fit Aware Expectation-confirmation Model towards Understanding of MOOCs Continued Usage Intention

Massive Open Online Courses (MOOCs) have been playing a pivotal role among the latest e-learning initiative and obtain widespread popularity in many universities. But the low course completion rate and the high midway dropout rate of students have puzzled some researchers and designers of MOOCs. Therefore, it is important to explore the factors affecting students’ continuance intention to use MOOCs. This study integrates task-technology fit which can explain how the characteristics of task and technology affect the outcome of technology utilization into expectation-confirmation model to analyze the factors influencing students’ keeping using MOOCs and the relationships of constructs in the model, then it will also extend our understandings of continuance intention about MOOCs. We analyze and study 234 respondents, and results reveal that perceived usefulness, satisfaction and task-technology fit are important precedents of the intention to continue using MOOCs. Researchers and designers of MOOCs may obtain further insight in continuance intention about MOOCs

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics.Comment: Accepted at ACL202

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey

Transferability estimation has been attached to great attention in the computer vision fields. Researchers try to estimate with low computational cost the performance of a model when transferred from a source task to a given target task. Considering the effectiveness of such estimations, the communities of natural language processing also began to study similar problems for the selection of pre-trained language models. However, there is a lack of a comprehensive comparison between these estimation methods yet. Also, the differences between vision and language scenarios make it doubtful whether previous conclusions can be established across fields. In this paper, we first conduct a thorough survey of existing transferability estimation methods being able to find the most suitable model, then we conduct a detailed empirical study for the surveyed methods based on the GLUE benchmark. From qualitative and quantitative analyses, we demonstrate the strengths and weaknesses of existing methods and show that H-Score generally performs well with superiorities in effectiveness and efficiency. We also outline the difficulties of consideration of training details, applicability to text generation, and consistency to certain metrics which shed light on future directions.Comment: Accepted by Findings of EMNLP 202

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

An internal ribosomal entry site mediates redox-sensitive translation of Nrf2

Nrf2 plays pivotal roles in coordinating the antioxidant response and maintaining redox homeostasis. Nrf2 expression is exquisitely regulated; Nrf2 expression is suppressed under unstressed conditions but strikingly induced under oxidative stress. Previous studies showed that stress-induced Nrf2 up-regulation results from both the inhibition of Nrf2 degradation and enhanced Nrf2 translation. In the present study, we elucidate the mechanism underlying translational control of Nrf2. An internal ribosomal entry site (IRES) was identified within the 5′ untranslated region of human Nrf2 mRNA. The IRESNrf2 contains a highly conserved 18S rRNA binding site (RBS) that is required for internal initiation. This IRESNrf2 also contains a hairpin structured inhibitory element (IE) located upstream of the RBS. Deletion of this IE remarkably enhanced translation. Significantly, treatment of cells with hydrogen peroxide (H2O2) and phyto-oxidant sulforaphane further stimulated IRESNrf2-mediated translation initiation despite the attenuation of global protein synthesis. Polyribosomal profile assay confirmed that endogenous Nrf2 mRNAs were recruited into polysomal fractions under oxidative stress conditions. Collectively, these data demonstrate that Nrf2 translation is suppressed under normal conditions and specifically enhanced upon oxidant exposure by internal initiation, and provide a mechanistic explanation for translational control of Nrf2 by oxidative stress

A Survey of Context Aware Web Service Discovery: From User's Perspective

Abstract—Web service is one of the fundamental technologies in implementing Service Oriented Architecture (SOA) based applications. One of the essential challenges in web service based applications is how to find a set of suitable web service candidates with regard to a user’s requirement. Currently most web service discovery systems have constrains on the content and format of submitted web service request. There-fore, during the information transformation from a user’s real web service need to formalized request, some useful informa-tion is lost implicitly or explicitly. To understand the user’s intention as much as possible, a lot of approaches have been proposed among them context aware methods have shown promising result. In this paper, we present an overview of the field of context aware web service discovery and try to classify current generation of those approaches into different catego-ries. This paper also describes limitations of current context awareness in web service discovery and discusses possible ap-plications that can enhance the overall discovery performance. Keywords-web service; discovery; survey; context awareness; applications I