9 citations · 29 across the 9 of their papers we have counts for
9 papers
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
Haonan Yin, Guanlong Jiao, Qianhui Wu +3
In the context of flexible manufacturing systems that are required to produce different types and quantities of products with minimal reconfiguration, this paper addresses the prob…
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Tingting Ma, Qianhui Wu, Huiqiang Jiang +3
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous…
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
Qianhui Wu, Huiqiang Jiang, Haonan Yin +2
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples. These ap…
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
Yiheng Shu, Zhiwei Yu, Yuhan Li +4
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization setting…
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
Wanjun Zhong, Tingting Ma, Jiahai Wang +4
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making. Inspired by dual…
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
Carina Negreanu, Alperen Karaoglu, Jack Williams +4
Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populati…
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Tingting Ma, Huiqiang Jiang, Qianhui Wu +2
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-lear…
Improving Entity Linking by Modeling Latent Entity Type Information
Shuang Chen, Jinpeng Wang, Feng Jiang +1
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to asse…
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
Qianhui Wu, Zijia Lin, Guoxin Wang +4
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing metho…