Search Results for author: Lidan Shou

Found 6 papers, 4 papers with code

SkipBERT: Efficient Inference with Shallow Layer Skipping

1 code implementation ACL 2022 Jue Wang, Ke Chen, Gang Chen, Lidan Shou, Julian McAuley

In this paper, we propose SkipBERT to accelerate BERT inference by skipping the computation of shallow layers.

Effective Slot Filling via Weakly-Supervised Dual-Model Learning

1 code implementation AAAI 2021 Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Gang Chen

By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach.

Slot Filling Spoken Language Understanding

LINDT: Tackling Negative Federated Learning with Local Adaptation

no code implementations23 Nov 2020 Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu

On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.

Federated Learning

An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing

1 code implementation8 Oct 2020 Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou

Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.

Databases Data Structures and Algorithms

Pyramid: A Layered Model for Nested Named Entity Recognition

2 code implementations ACL 2020 Jue Wang, Lidan Shou, Ke Chen, Gang Chen

Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.

named-entity-recognition NER +1

Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction

no code implementations8 Apr 2019 Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Sharad Mehrotra

In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself.

Few-Shot Learning

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