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Found 10 papers, 1 papers with code
Coarse-to-Fine Contrastive Learning on Graphs
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.
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
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.
To be Closer: Learning to Link up Aspects with Opinions
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning
To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense reasoning (VCR) task has been introduced.
Ranked #4 on
Visual Question Answering (VQA)
on VCR (Q-AR) test
Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
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
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.
Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
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.
Ranked #6 on
Sentence Compression
on Google Dataset
Aligning Users Across Social Networks Using Network Embedding
Li Liu, 1 William K. Cheung, 2 Xin Li, 1⇤ and Lejian Liao1
