2.2k citations · 2.3k across the 8 of their papers we have counts for
7 papers · 1 filter
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Siddharth Varia, Shuai Wang, Kishaloy Halder +7
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term,…
Label Semantics for Few Shot Named Entity Recognition
Jie Ma, Miguel Ballesteros, Srikanth Doss +4
We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model a…
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Elsbeth Turcan, Shuai Wang, Rishita Anubhai +3
Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an e…
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai +4
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 r…
Resource-Enhanced Neural Model for Event Argument Extraction
Jie Ma, Shuai Wang, Rishita Anubhai +2
Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain…
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros, Rishita Anubhai, Shuai Wang +6
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events wit…
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg +31
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pip…