145 citations · 265 across the 18 of their papers we have counts for
19 papers
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
Qili Zhang, Qianren Mao, Yangyifei Luo +15
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
Mingxin Li, Richong Zhang, Zhijie Nie +1
Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream t…
Code-Style In-Context Learning for Knowledge-Based Question Answering
Zhijie Nie, Richong Zhang, Zhongyuan Wang +1
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical application…
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
Junfan Chen, Richong Zhang, Yongyi Mao +1
The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) mode…
Neural Dialogue State Tracking with Temporally Expressive Networks
Junfan Chen, Richong Zhang, Yongyi Mao +1
Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to exp…
Regularizing Neural Networks via Adversarial Model Perturbation
Yaowei Zheng, Richong Zhang, Yongyi Mao
Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, ba…
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
Dezhi Liu, Richong Zhang, Ziqiao Wang
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, r…
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
Kai Sun, Richong Zhang, Samuel Mensah +2
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the…
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh +2
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation lea…
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Junfan Chen, Richong Zhang, Yongyi Mao +2
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of th…
MixUp as Directional Adversarial Training
Guillaume P. Archambault, Yongyi Mao, Hongyu Guo +1
In this work, we explain the working mechanism of MixUp in terms of adversarial training. We introduce a new class of adversarial training schemes, which we refer to as directional…
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Hongyu Guo, Yongyi Mao, Richong Zhang
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly imp…
Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks
Bokang Zhu, Richong Zhang, Dingkun Long +1
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent net…
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
MixUp is a recently proposed data-augmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their label…
Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process
Peiyuan Suny, Jianxin Li, Yongyi Mao +2
Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of ev…
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization
Hongyu Guo, Yongyi Mao, Ali Al-Bashabsheh +1
Based on the notion of information bottleneck (IB), we formulate a quantization problem called "IB quantization". We show that IB quantization is equivalent to learning based on th…
Prototypical Recurrent Unit
Dingkun Long, Richong Zhang, Yongyi Mao
Despite the great successes of deep learning, the effectiveness of deep neural networks has not been understood at any theoretical depth. This work is motivated by the thrust of de…
On the representation and embedding of knowledge bases beyond binary relations
Jianfeng Wen, Jianxin Li, Yongyi Mao +2
The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary. For the training and testing o…
A Cooperation Model Towards the Federated Internet of Applications
Zhe Zhang, Xudong Liu, Hailong Sun +2
As Internet is changing from network of data into network of functionalities, a federated Internet of applications, that every application can cooperate with each other smoothly, i…