36 citations · 68 across the 10 of their papers we have counts for
10 papers
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures
Jinjie Ni, Fuzhao Xue, Xiang Yue +5
Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-…
A Survey on Semantic Processing Techniques
Rui Mao, Kai He, Xulang Zhang +4
Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of…
Finding the Pillars of Strength for Multi-Head Attention
Jinjie Ni, Rui Mao, Zonglin Yang +2
Recent studies have revealed some issues of Multi-Head Attention (MHA), e.g., redundancy and over-parameterization. Specifically, the heads of MHA were originally designed to atten…
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models
Jinjie Ni, Yukun Ma, Wen Wang +7
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models. With knowledge distillation, these models may also benefit from the…
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
Zonglin Yang, Xinya Du, Rui Mao +2
Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses forma…
deHuBERT: Disentangling Noise in a Self-supervised Model for Robust Speech Recognition
Dianwen Ng, Ruixi Zhang, Jia Qi Yip +7
Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However,…
A Class-Aware Representation Refinement Framework for Graph Classification
Jiaxing Xu, Jinjie Ni, Yiping Ke
Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect…
Fusing task-oriented and open-domain dialogues in conversational agents
Tom Young, Frank Xing, Vlad Pandelea +2
The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform goal-oriented functio…
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
Jinjie Ni, Tom Young, Vlad Pandelea +2
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving stu…
An Embarrassingly Simple Model for Dialogue Relation Extraction
Fuzhao Xue, Aixin Sun, Hao Zhang +2
Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we propose a simple yet effective model named SimpleRE for…