Search Results for author: Lejian Liao

Found 10 papers, 1 papers with code

Coarse-to-Fine Contrastive Learning on Graphs

no code implementations13 Dec 2022 Peiyao Zhao, Yuangang Pan, Xin Li, Xu Chen, Ivor W. Tsang, Lejian Liao

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner.

Contrastive Learning Learning-To-Rank

MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding

no code implementations27 Nov 2022 Meihuizi Jia, Lei Shen, Xin Shen, Lejian Liao, Meng Chen, Xiaodong He, Zhendong Chen, Jiaqi Li

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.

named-entity-recognition Named Entity Recognition +4

KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning

no code implementations13 Dec 2020 Dandan song, Siyi Ma, Zhanchen Sun, Sicheng Yang, Lejian Liao

To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense reasoning (VCR) task has been introduced.

Sentence Visual Commonsense Reasoning +1

Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis

no code implementations19 May 2019 Bowen Xing, Lejian Liao, Dandan song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, He-Yan Huang

This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)

Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction

no code implementations EMNLP 2018 Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, He-Yan Huang

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.

Feature Engineering Relation +2

Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains

no code implementations ACL 2017 Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, D. Song, an, Lejian Liao

In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression.

Sentence Sentence Compression

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