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Found 16 papers, 14 papers with code
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching
Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning
While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation.
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.
Claim-Evidence Pair Extraction (CEPE)
Claim Extraction with Stance Classification (CESC)
+1
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1. 36 F1 and 1. 46 Ign_F1 score on the DocRED leaderboard.
Ranked #2 on
Relation Extraction
on DocRED
Document-level Relation Extraction
Knowledge Distillation
+2
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
Bootstrapped Unsupervised Sentence Representation Learning
As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention.
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
Experimental results show that our proposed method achieves significant performance improvements over the state-of-the-art pretrained cross-lingual language model in the CLCD setting.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.
Ranked #20 on
Semantic Textual Similarity
on STS16
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain.
Effective Attention Modeling for Aspect-Level Sentiment Classification
First, we propose a method for target representation that better captures the semantic meaning of the opinion target.
Exploiting Document Knowledge for Aspect-level Sentiment Classification
Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification.
An Unsupervised Neural Attention Model for Aspect Extraction
Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.
