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20182022
most citedUsing Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks

43 citations · 57 across the 7 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL2022

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

Erxin Yu, Lan Du, Yuan Jin +2

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable perfo…

cs.CL2021

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction

Bo Wang, Tao Shen, Guodong Long +2

Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment a…

cs.CL202143 cited

Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks

Tingyu Xia, Yue Wang, Yuan Tian +1

We study the problem of incorporating prior knowledge into a deep Transformer-based model,i.e.,Bidirectional Encoder Representations from Transformers (BERT), to enhance its perfor…

cs.CL2020

Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

Bo Wang, Tao Shen, Guodong Long +2

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion…

cs.CL2019

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Zhepei Wei, Jianlin Su, Yue Wang +2

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple pr…

cs.CL2018

Zero-shot User Intent Detection via Capsule Neural Networks

Congying Xia, Chenwei Zhang, Xiaohui Yan +2

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are la…