49 citations · 101 across the 18 of their papers we have counts for
18 papers
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Yinghui Li, Haojing Huang, Shirong Ma +5
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. Benefiting from their emergent abilities, LLMs have…
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
Hengyuan Zhang, Dawei Li, Yanran Li +3
The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated def…
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
Yue He, Zimu Wang, Peng Cui +4
In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to…
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation
Ke Zhang, Sihong Chen, Qi Ju +3
With the increase in the number of image data and the lack of corresponding labels, weakly supervised learning has drawn a lot of attention recently in computer vision tasks, espec…
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Yu Zhang, Qingrong Xia, Shilin Zhou +3
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiqui…
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Xinyin Ma, Yong Jiang, Nguyen Bach +4
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entit…
DGEM: A New Dual-modal Graph Embedding Method in Recommendation System
Huimin Zhou, Qing Li, Yong Jiang +2
In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to t…
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings
Xinyu Wang, Zixia Jia, Yong Jiang +1
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatena…
Towards Emotional Support Dialog Systems
Siyang Liu, Chujie Zheng, Orianna Demasi +5
Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable proce…
Diversifying Dialog Generation via Adaptive Label Smoothing
Yida Wang, Yinhe Zheng, Yong Jiang +1
Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in…
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contex…
An Investigation of Potential Function Designs for Neural CRF
Zechuan Hu, Yong Jiang, Nguyen Bach +4
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential function…
Neural Latent Dependency Model for Sequence Labeling
Yang Zhou, Yong Jiang, Zechuan Hu +1
Sequence labeling is a fundamental problem in machine learning, natural language processing and many other fields. A classic approach to sequence labeling is linear chain condition…
Adversarial Attack and Defense of Structured Prediction Models
Wenjuan Han, Liwen Zhang, Yong Jiang +1
Building an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in rece…
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Xinyu Wang, Yong Jiang, Nguyen Bach +4
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forwa…
A Survey of Unsupervised Dependency Parsing
Wenjuan Han, Yong Jiang, Hwee Tou Ng +1
Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no anno…
More Embeddings, Better Sequence Labelers?
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings. However, there is no definite c…
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
Xinyu Wang, Yong Jiang, Kewei Tu
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Ta…