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Neural Word Decomposition Models for Abusive Language Detection
User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc.
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
PerKGQA: Question Answering over Personalized Knowledge Graphs
The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG.
What Do Users Care About? Detecting Actionable Insights from User Feedback
Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line.
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels
Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
