Search Results for author: Shijin Wang

Found 56 papers, 27 papers with code

Survey of Computerized Adaptive Testing: A Machine Learning Perspective

1 code implementation31 Mar 2024 Qi Liu, Yan Zhuang, Haoyang Bi, Zhenya Huang, Weizhe Huang, Jiatong Li, Junhao Yu, Zirui Liu, Zirui Hu, Yuting Hong, Zachary A. Pardos, Haiping Ma, Mengxiao Zhu, Shijin Wang, Enhong Chen

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.

cognitive diagnosis Question Selection +1

Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing

1 code implementation10 Mar 2024 Liyang He, Zhenya Huang, Jiayu Liu, Enhong Chen, Fei Wang, Jing Sha, Shijin Wang

In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.

Image Retrieval Knowledge Distillation +1

Unified Uncertainty Estimation for Cognitive Diagnosis Models

no code implementations9 Mar 2024 Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang

Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets.

cognitive diagnosis

Generative Input: Towards Next-Generation Input Methods Paradigm

no code implementations2 Nov 2023 Keyu Ding, Yongcan Wang, Zihang Xu, Zhenzhen Jia, Shijin Wang, Cong Liu, Enhong Chen

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.

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning

1 code implementation27 Jun 2023 Zihang Xu, Ziqing Yang, Yiming Cui, Shijin Wang

IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2. 0 while keeping competitive general language understanding ability through testing on tasks in GLUE.

Logical Reasoning Machine Reading Comprehension

Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective

1 code implementation18 Jun 2023 Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Qingyang Mao, Shijin Wang, Enhong Chen

Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models.

Mathematical Reasoning

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

1 code implementation NeurIPS 2023 Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}.

Math

CSED: A Chinese Semantic Error Diagnosis Corpus

no code implementations9 May 2023 Bo Sun, Baoxin Wang, YiXuan Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Ting Liu

Our experiments show that powerful pre-trained models perform poorly on this corpus.

Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

1 code implementation18 Jan 2023 Yuting Ning, Zhenya Huang, Xin Lin, Enhong Chen, Shiwei Tong, Zheng Gong, Shijin Wang

To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.

Contrastive Learning

Gradient-based Intra-attention Pruning on Pre-trained Language Models

1 code implementation15 Dec 2022 Ziqing Yang, Yiming Cui, Xin Yao, Shijin Wang

In this work, we propose a structured pruning method GRAIN (Gradient-based Intra-attention pruning), which performs task-specific pruning with knowledge distillation and yields highly effective models.

Knowledge Distillation

LERT: A Linguistically-motivated Pre-trained Language Model

1 code implementation10 Nov 2022 Yiming Cui, Wanxiang Che, Shijin Wang, Ting Liu

We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.

Language Modelling Stock Market Prediction +1

Overview of CTC 2021: Chinese Text Correction for Native Speakers

1 code implementation11 Aug 2022 Honghong Zhao, Baoxin Wang, Dayong Wu, Wanxiang Che, Zhigang Chen, Shijin Wang

In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers.

JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding

1 code implementation13 Jun 2022 Wayne Xin Zhao, Kun Zhou, Zheng Gong, Beichen Zhang, Yuanhang Zhou, Jing Sha, Zhigang Chen, Shijin Wang, Cong Liu, Ji-Rong Wen

Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.

Language Modelling Math

Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training

no code implementations28 Feb 2022 Ziqing Yang, Yiming Cui, Zhigang Chen, Shijin Wang

In this paper, we aim to improve the multilingual model's supervised and zero-shot performance simultaneously only with the resources from supervised languages.

Language Modelling text-classification +1

Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models

no code implementations26 Aug 2021 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Question Answering +1

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

1 code implementation10 May 2021 Yiming Cui, Ting Liu, Wanxiang Che, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Multi-Choice MRC +2

Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering

no code implementations7 Feb 2021 Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them.

Information Retrieval Multi-hop Question Answering +2

Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

no code implementations15 Jan 2021 Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, Shijin Wang

In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.

Active Learning

Unsupervised Explanation Generation for Machine Reading Comprehension

no code implementations13 Nov 2020 Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances.

Explanation Generation Machine Reading Comprehension +1

CharBERT: Character-aware Pre-trained Language Model

1 code implementation COLING 2020 Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable.

Language Modelling Question Answering +3

Revisiting Pre-Trained Models for Chinese Natural Language Processing

6 code implementations Findings of the Association for Computational Linguistics 2020 Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

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 pre-trained language models.

Language Modelling Stock Market Prediction

Benchmarking Robustness of Machine Reading Comprehension Models

1 code implementation Findings (ACL) 2021 Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of adversarial attacks, including our novel distractor extraction and generation attacks.

Benchmarking Machine Reading Comprehension +1

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension

1 code implementation COLING 2020 Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).

Machine Reading Comprehension Sentence

Is Graph Structure Necessary for Multi-hop Question Answering?

no code implementations EMNLP 2020 Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop question answering.

Graph Attention Multi-hop Question Answering +1

Discriminative Sentence Modeling for Story Ending Prediction

no code implementations19 Dec 2019 Yiming Cui, Wanxiang Che, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.

Cloze Test Sentence

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

no code implementations19 Dec 2019 Xingyi Duan, Baoxin Wang, Ziyue Wang, Wentao Ma, Yiming Cui, Dayong Wu, Shijin Wang, Ting Liu, Tianxiang Huo, Zhen Hu, Heng Wang, Zhiyuan Liu

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.

Machine Reading Comprehension

Contextual Recurrent Units for Cloze-style Reading Comprehension

no code implementations14 Nov 2019 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhipeng Chen, Shijin Wang, Guoping Hu

Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.

Reading Comprehension Sentence +2

Improving Machine Reading Comprehension via Adversarial Training

no code implementations9 Nov 2019 Ziqing Yang, Yiming Cui, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.

General Classification Image Classification +3

IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis

no code implementations IJCNLP 2019 Ziyue Wang, Baoxin Wang, Xingyi Duan, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu

To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients.

Natural Language Inference Question Answering +1

Neural Cognitive Diagnosis for Intelligent Education Systems

1 code implementation23 Aug 2019 Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.

cognitive diagnosis

QuesNet: A Unified Representation for Heterogeneous Test Questions

no code implementations27 May 2019 Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang, Yu Su

Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.

Language Modelling

Exploiting Cognitive Structure for Adaptive Learning

no code implementations23 May 2019 Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang

Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.

Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

no code implementations21 Nov 2018 Zhipeng Chen, Yiming Cui, Wentao Ma, Shijin Wang, Guoping Hu

Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates.

Machine Reading Comprehension Multiple-choice

HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension

no code implementations15 Mar 2018 Zhipeng Chen, Yiming Cui, Wentao Ma, Shijin Wang, Ting Liu, Guoping Hu

This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.

Multiple-choice Reading Comprehension

Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution

no code implementations ACL 2017 Ting Liu, Yiming Cui, Qingyu Yin, Wei-Nan Zhang, Shijin Wang, Guoping Hu

Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers.

Reading Comprehension

LSTM Neural Reordering Feature for Statistical Machine Translation

no code implementations NAACL 2016 Yiming Cui, Shijin Wang, Jianfeng Li

Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling.

Language Modelling Machine Translation +1

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