Search Results for author: Xiao Ding

Found 36 papers, 16 papers with code

CogBERT: Cognition-Guided Pre-trained Language Models

1 code implementation COLING 2022 Xiao Ding, Bowen Chen, Li Du, Bing Qin, Ting Liu

To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.

EEG

Neural Natural Logic Inference for Interpretable Question Answering

1 code implementation EMNLP 2021 Jihao Shi, Xiao Ding, Li Du, Ting Liu, Bing Qin

Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.

Multiple-choice Natural Language Inference +1

Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation

1 code implementation2 Apr 2024 Zhouhao Sun, Xiao Ding, Li Du, Bibo Cai, Jinglong Gao, Ting Liu, Qin Bing

To address this issue, we propose a novel framework, named Generalizable and Faithful Reasoner (GFaiR), which introduces the paradigm of resolution refutation.

RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict

1 code implementation25 Mar 2024 Yirong Zeng, Xiao Ding, Yi Zhao, Xiangyu Li, Jie Zhang, Chao Yao, Ting Liu, Bing Qin

Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation.

16k Claim Verification +4

Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance

no code implementations14 Mar 2024 Kai Xiong, Xiao Ding, Ting Liu, Bing Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Yixin Cao

Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence.

Memorization

Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning

no code implementations18 Feb 2024 Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Zhouhao Sun, Jun Shi, Ting Liu, Bing Qin

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance.

Machine Unlearning

Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling

no code implementations20 Jun 2023 Linyao Yang, Hongyang Chen, Zhao Li, Xiao Ding, Xindong Wu

Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities.

Knowledge Graphs Language Modelling

Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

1 code implementation19 May 2023 Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin

Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus despite noticeable inter-inconsistencies, but imbalances in their abilities can lead to domination by superior LLMs.

Decision Making

NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing

1 code implementation18 May 2023 Tingting Wu, Xiao Ding, Minji Tang, Hao Zhang, Bing Qin, Ting Liu

To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.

Learning with noisy labels

ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks

1 code implementation16 Dec 2022 Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Bing Qin, Yi Zheng, Baoxing Huai

Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.

Decision Making

DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination

no code implementations21 Aug 2022 Tingting Wu, Xiao Ding, Hao Zhang, Jinglong Gao, Li Du, Bing Qin, Ting Liu

To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.

Image Classification regression

Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?

no code implementations14 Aug 2022 Bowen Chen, Xiao Ding, Li Du, Qin Bing, Ting Liu

Given a task, human learns from easy to hard, whereas the model learns randomly.

A Graph Enhanced BERT Model for Event Prediction

no code implementations Findings (ACL) 2022 Li Du, Xiao Ding, Yue Zhang, Kai Xiong, Ting Liu, Bing Qin

To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

1 code implementation ACL 2022 Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin

Understanding causality has vital importance for various Natural Language Processing (NLP) applications.

valid

Learning Event Graph Knowledge for Abductive Reasoning

1 code implementation ACL 2021 Li Du, Xiao Ding, Ting Liu, Bing Qin

Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.

Question Answering Reading Comprehension

ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning

1 code implementation ACL 2021 Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin

ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.

Representation Learning

CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision

no code implementations21 Jul 2021 Zhongyang Li, Xiao Ding, Kuo Liao, Bing Qin, Ting Liu

Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.

Causal Inference

Guided Generation of Cause and Effect

no code implementations21 Jul 2021 Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme

We present a conditional text generation framework that posits sentential expressions of possible causes and effects.

Conditional Text Generation Knowledge Graphs

Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder

no code implementations IJCNLP 2019 Li Du, Xiao Ding, Ting Liu, Zhongyang Li

Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP).

Event Representation Learning Enhanced with External Commonsense Knowledge

1 code implementation IJCNLP 2019 Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan

Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.

Representation Learning Stock Market Prediction

ELG: An Event Logic Graph

no code implementations18 Jul 2019 Xiao Ding, Zhongyang Li, Ting Liu, Kuo Liao

The evolution and development of events have their own basic principles, which make events happen sequentially.

Decision Making

Story Ending Prediction by Transferable BERT

1 code implementation17 May 2019 Zhongyang Li, Xiao Ding, Ting Liu

In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task.

Language Modelling Natural Language Inference +2

Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction

1 code implementation COLING 2018 Junwen Duan, Yue Zhang, Xiao Ding, Ching-Yun Chang, Ting Liu

The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.

Information Retrieval Sentence +1

Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training

no code implementations COLING 2018 Zhongyang Li, Xiao Ding, Ting Liu

In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context.

Cloze Test Information Retrieval +1

Learning Sentence Representations over Tree Structures for Target-Dependent Classification

no code implementations NAACL 2018 Junwen Duan, Xiao Ding, Ting Liu

To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures.

Classification Decoder +5

Constructing Narrative Event Evolutionary Graph for Script Event Prediction

1 code implementation14 May 2018 Zhongyang Li, Xiao Ding, Ting Liu

Script event prediction requires a model to predict the subsequent event given an existing event context.

Graph Neural Network Multiple-choice

Knowledge-Driven Event Embedding for Stock Prediction

no code implementations COLING 2016 Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications.

Information Retrieval Open Information Extraction +4

A General Framework for Content-enhanced Network Representation Learning

no code implementations10 Oct 2016 Xiaofei Sun, Jiang Guo, Xiao Ding, Ting Liu

This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks.

Network Embedding Node Classification

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