Hao Peng

News Citation Recommendation with Implicit and Explicit Semantics

2016

In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. Due to the sparsity issue …

Learning from Context or Names? An Empirical Study on Neural Relation Extraction

2020

Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding which type of information affects existing RE models to make decisions and how to further improve the performance …

Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation

2020 · arXiv (Cornell University)

Much recent effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to the latter, where generation is sequential, …

Federated Multi-view Learning for Private Medical Data Integration and Analysis

2022 · ACM Transactions on Intelligent Systems and Technology

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in the medical field. Two critical challenges are identified: …

Tailor: Generating and Perturbing Text with Semantic Controls

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, which is expensive and hard to generalize. We present Tailor, a semantically-controlled …

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

2021 · arXiv (Cornell University)

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an …

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2022 · arXiv (Cornell University)

Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive …

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

2022 · arXiv (Cornell University)

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due …

Reinforcement Learning Guided Multi-Objective Exam Paper Generation

2023 · Society for Industrial and Applied Mathematics eBooks

To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified …

Modeling Context With Linear Attention for Scalable Document-Level Translation

2022

Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long documents as their attention layers have quadratic complexity in …

Zero-Shot Text Normalization via Cross-Lingual Knowledge Distillation

2024 · IEEE/ACM Transactions on Audio Speech and Language Processing

Text normalization (TN) is a crucial preprocessing step in text-to-speech synthesis, which pertains to the accurate pronunciation of numbers and symbols within the text. Existing neural network-based TN methods have shown significant success in rich-resource …

Seismic Noise Suppression Method Based on Wave-Unet and Attention Mechanism

2024 · IEEE Transactions on Geoscience and Remote Sensing

Seismic noise suppression refers to a data processing technique utilized to bolster the signal-to-noise ratio of recorded seismic signals. This enhancement in clarity can significantly amplify the efficacy of subsequent analysis and processing endeavors. With …

Prompt-based Unifying Inference Attack on Graph Neural Networks

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, GNNs' predictive performance relies on the …

Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems

2025

Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both …

SetKE: Knowledge Editing for Knowledge Elements Overlap

2025

Large Language Models (LLMs) excel in tasks such as retrieval and question answering but require updates to incorporate new knowledge and reduce inaccuracies and hallucinations. Traditional updating methods, like fine-tuning and incremental learning, face challenges …

Evolving Graph Learning for Out-of-Distribution Generalization in Non-Stationary Environments

2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability under distribution shifts, which is inevitable in dynamic scenarios. As dynamic …

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

2015 · arXiv (Cornell University)

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. …

Complexity-Based Prompting for Multi-Step Reasoning

2022 · arXiv (Cornell University)

We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a …