98 citations · 128 across the 16 of their papers we have counts for
16 papers
Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in Large Language Models
Yachao Zhao, Bo Wang, Dongming Zhao +4
Recent researches indicate that Pre-trained Large Language Models (LLMs) possess cognitive constructs similar to those observed in humans, prompting researchers to investigate the…
Syntax-Aware Complex-Valued Neural Machine Translation
Yang Liu, Yuexian Hou
Syntax has been proven to be remarkably effective in neural machine translation (NMT). Previous models obtained syntax information from syntactic parsing tools and integrated it in…
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona
Yihong Tang, Bo Wang, Miao Fang +4
The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three…
Constructing Holistic Measures for Social Biases in Masked Language Models
Yang Liu, Yuexian Hou
Masked Language Models (MLMs) have been successful in many natural language processing tasks. However, real-world stereotype biases are likely to be reflected in MLMs due to their…
Empathetic Response Generation via Emotion Cause Transition Graph
Yushan Qian, Bo Wang, Ting-En Lin +6
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besid…
Mining Effective Features Using Quantum Entropy for Humor Recognition
Yang Liu, Yuexian Hou
Humor recognition has been extensively studied with different methods in the past years. However, existing studies on humor recognition do not understand the mechanisms that genera…
Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation
Yushan Qian, Bo Wang, Shangzhao Ma +5
Towards human-like dialogue systems, current emotional dialogue approaches jointly model emotion and semantics with a unified neural network. This strategy tends to generate safe r…
Aligning Recommendation and Conversation via Dual Imitation
Jinfeng Zhou, Bo Wang, Minlie Huang +4
Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations…
Label-Based Diversity Measure Among Hidden Units of Deep Neural Networks: A Regularization Method
Chenguang Zhang, Yuexian Hou, Dawei Song +2
Although the deep structure guarantees the powerful expressivity of deep networks (DNNs), it also triggers serious overfitting problem. To improve the generalization capacity of DN…
A Tensorized Transformer for Language Modeling
Xindian Ma, Peng Zhang, Shuai Zhang +4
Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, h…
Quantum observation scheme universally identifying causalities from correlations
Chenguang Zhang, Yuexian Hou, Dawei Song
It has long been recognized as a difficult problem to determine whether the observed statistical correlation between two classical variables arise from causality or from common cau…
Discrimination Between Quantum Common Causes and Quantum Causality
Mingdi Hu, Yuexian Hou
In classic cases, Reichenbach's principle implies that discriminating between common causes and causality is unprincipled since the discriminative results essentially depend on the…
Further Theoretical Study of Distribution Separation Method for Information Retrieval
Peng Zhang, Qian Yu, Yuexian Hou +3
Recently, a Distribution Separation Method (DSM) is proposed for relevant feedback in information retrieval, which aims to approximate the true relevance distribution by separating…
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines
Xiaozhao Zhao, Yuexian Hou, Dawei Song +1
Typical dimensionality reduction (DR) methods are often data-oriented, focusing on directly reducing the number of random variables (features) while retaining the maximal variation…
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle
Xiaozhao Zhao, Yuexian Hou, Qian Yu +2
Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data. In this paper, we consider the di…
On Tsallis Entropy Bias and Generalized Maximum Entropy Models
Yuexian Hou, Tingxu Yan, Peng Zhang +2
In density estimation task, maximum entropy model (Maxent) can effectively use reliable prior information via certain constraints, i.e., linear constraints without empirical parame…