127 citations · 498 across the 73 of their papers we have counts for
74 papers
Multi-grained Evidence Inference for Multi-choice Reading Comprehension
Yilin Zhao, Hai Zhao, Sufeng Duan
Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options. Answers in multi-choice MRC cannot…
Multi-turn Dialogue Comprehension from a Topic-aware Perspective
Xinbei Ma, Yi Xu, Hai Zhao +1
Dialogue related Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the int…
Baichuan 2: Open Large-scale Language Models
Aiyuan Yang, Bin Xiao, Bingning Wang +52
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing…
A Compact Variable Stiffness Actuator for Agile Legged Locomotion
Lei Yu, Haizhou Zhao, Siying Qin +2
The legged robots with variable stiffness actuators (VSAs) can achieve energy-efficient and versatile locomotion. However, equipping legged robots with VSAs in real-world applicati…
Design and Control of a Bio-inspired Wheeled Bipedal Robot
Haizhou Zhao, Lei Yu, Siying Qin +2
Wheeled bipedal robots (WBRs) have the capability to execute agile and versatile locomotion tasks. This paper focuses on improving the dynamic performance of WBRs through innovatio…
Extrapolating Multilingual Understanding Models as Multilingual Generators
Bohong Wu, Fei Yuan, Hai Zhao +2
Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT).…
Solution of DeBERTaV3 on CommonsenseQA
Letian Peng, Zuchao Li, Hai Zhao
We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language…
Adversarial Self-Attention for Language Understanding
Hongqiu Wu, Ruixue Ding, Hai Zhao +3
Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness…
Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
Yiyang Li, Hai Zhao, Zhuosheng Zhang
Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provid…
Lite Unified Modeling for Discriminative Reading Comprehension
Yilin Zhao, Hai Zhao, Libin Shen +1
As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials. However, the focuses…
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model
Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang +1
Recently, the problem of robustness of pre-trained language models (PrLMs) has received increasing research interest. Latest studies on adversarial attacks achieve high attack succ…
ArT: All-round Thinker for Unsupervised Commonsense Question-Answering
Jiawei Wang, Hai Zhao
Without labeled question-answer pairs for necessary training, unsupervised commonsense question-answering (QA) appears to be extremely challenging due to its indispensable unique p…
Advances in Multi-turn Dialogue Comprehension: A Survey
Zhuosheng Zhang, Hai Zhao
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been desi…
Structural Characterization for Dialogue Disentanglement
Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, in…
Tracing Origins: Coreference-aware Machine Reading Comprehension
Baorong Huang, Zhuosheng Zhang, Hai Zhao
Machine reading comprehension is a heavily-studied research and test field for evaluating new pre-trained language models (PrLMs) and fine-tuning strategies, and recent studies hav…
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval
Bohong Wu, Zhuosheng Zhang, Jinyuan Wang +1
Training dense passage representations via contrastive learning has been shown effective for Open-Domain Passage Retrieval (ODPR). Existing studies focus on further optimizing by i…
Advances in Multi-turn Dialogue Comprehension: A Survey
Zhuosheng Zhang, Hai Zhao
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been desi…
Text Compression-aided Transformer Encoding
Zuchao Li, Zhuosheng Zhang, Hai Zhao +4
Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transfo…
Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge
Zhuosheng Zhang, Junlong Li, Hai Zhao
Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. The major challenges…
SG-Net: Syntax Guided Transformer for Language Representation
Zhuosheng Zhang, Yuwei Wu, Junru Zhou +3
Understanding human language is one of the key themes of artificial intelligence. For language representation, the capacity of effectively modeling the linguistic knowledge from th…
To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph
Sufeng Duan, Hai Zhao
In this paper, we propose an explanation of representation for self-attention network (SAN) based neural sequence encoders, which regards the information captured by the model and…
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment
Yian Li, Hai Zhao
Although pre-trained contextualized language models such as BERT achieve significant performance on various downstream tasks, current language representation still only focuses on…
Accurate Word Representations with Universal Visual Guidance
Zhuosheng Zhang, Haojie Yu, Hai Zhao +2
Word representation is a fundamental component in neural language understanding models. Recently, pre-trained language models (PrLMs) offer a new performant method of contextualize…
Enhancing Pre-trained Language Model with Lexical Simplification
Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang +1
For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given…