12 citations · 29 across the 52 of their papers we have counts for
15 papers · 1 filter
Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability
Jishnu Ray Chowdhury, Cornelia Caragea
Binary Balanced Tree RvNNs (BBT-RvNNs) enforce sequence composition according to a preset balanced binary tree structure. Thus, their non-linear recursion depth is just …
Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion
Smriti Singh, Cornelia Caragea, Junyi Jessy Li
Situations and events evoke emotions in humans, but to what extent do they inform the prediction of emotion detection models? This work investigates how well human-annotated emotio…
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification
Henry Peng Zou, Yue Zhou, Cornelia Caragea +1
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and…
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
Henry Peng Zou, Cornelia Caragea
Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suff…
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
Henry Peng Zou, Yue Zhou, Weizhi Zhang +1
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organi…
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins
Tiberiu Sosea, Cornelia Caragea
We introduce MarginMatch, a new SSL approach combining consistency regularization and pseudo-labeling, with its main novelty arising from the use of unlabeled data training dynamic…
Sarcasm Detection in a Disaster Context
Tiberiu Sosea, Junyi Jessy Li, Cornelia Caragea
During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about…
Efficient Beam Tree Recursion
Jishnu Ray Chowdhury, Cornelia Caragea
Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as a simple extension of Gumbel Tree RvNN and it was shown to achieve state-of-the-art length generalization perf…
Beam Tree Recursive Cells
Jishnu Ray Chowdhury, Cornelia Caragea
We propose Beam Tree Recursive Cell (BT-Cell) - a backpropagation-friendly framework to extend Recursive Neural Networks (RvNNs) with beam search for latent structure induction. We…
Unsupervised Extractive Summarization of Emotion Triggers
Tiberiu Sosea, Hongli Zhan, Junyi Jessy Li +1
Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoin…
Monotonic Location Attention for Length Generalization
Jishnu Ray Chowdhury, Cornelia Caragea
We explore different ways to utilize position-based cross-attention in seq2seq networks to enable length generalization in algorithmic tasks. We show that a simple approach of inte…
Data Augmentation for Low-Resource Keyphrase Generation
Krishna Garg, Jishnu Ray Chowdhury, Cornelia Caragea
Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale…
DMDD: A Large-Scale Dataset for Dataset Mentions Detection
Huitong Pan, Qi Zhang, Eduard Dragut +2
The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportun…
Neural Keyphrase Generation: Analysis and Evaluation
Tuhin Kundu, Jishnu Ray Chowdhury, Cornelia Caragea
Keyphrase generation aims at generating topical phrases from a given text either by copying from the original text (present keyphrases) or by producing new keyphrases (absent keyph…
Semantic Tokenizer for Enhanced Natural Language Processing
Sandeep Mehta, Darpan Shah, Ravindra Kulkarni +1
Traditionally, NLP performance improvement has been focused on improving models and increasing the number of model parameters. NLP vocabulary construction has remained focused on m…