• Publications
  • Influence
Stop Clickbait: Detecting and preventing clickbaits in online news media
TLDR
This work attempts to automatically detect clickbait detection and then builds a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines, and offers each reader an option to block clickbaits she doesn't want to see. Expand
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Several real-world applications require real-time prediction on resource-scarce devices such as an Internet of Things (IoT) sensor. Such applications demand prediction models with small storage andExpand
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
TLDR
Through extensive experiments on multi-class and multi-label classification tasks, this work outperforms the previous state-of-the-art method, NTSG and achieves a significant reduction in training and prediction times compared to other representation methods. Expand
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
TLDR
This paper shows that it is possible to better manage this trade-off by optimizing a bound on the Information Bottleneck (IB) objective, and derives a learning objective that allows direct control of mask sparsity levels through a tunable sparse prior. Expand
Entity Projection via Machine Translation for Cross-Lingual NER
TLDR
This work proposes a system that improves over prior entity-projection methods by leveraging machine translation systems twice: first for translating sentences and subsequently for translating entities; and matching entities based on orthographic and phonetic similarity; and identifying matches based on distributional statistics derived from the dataset. Expand
Predicting the Temporal and Social Dynamics of Curiosity in Small Group Learning
TLDR
A model that predicts the temporal and social dynamics of curiosity based on sequences of behaviors exhibited by individuals engaged in group learning is presented, revealing distinct sequential behavior patterns that predict increase and decrease of curiosity in individuals, and convergence to high and low curiosity among group members. Expand
Contextualized Representations for Low-resource Utterance Tagging
TLDR
Unsupervised training of utterance representations from a large corpus of spontaneous dialogue data achieve competitive performance on utterance-level dialogue-act recognition and emotion classification, especially in low-resource settings encountered when analyzing conversations in new domains. Expand
Prompting Contrastive Explanations for Commonsense Reasoning Tasks
TLDR
Inspired by the contrastive nature of human explanations, large pretrained language models are used to complete explanation prompts which contrast alternatives according to the key attribute(s) required to justify the correct answer. Expand
EASE: Extractive-Abstractive Summarization with Explanations
TLDR
This work presents an explainable summarization system based on the Information Bottleneck principle that is jointly trained for extraction and abstraction in an end-to-end fashion and shows that explanations from this framework are more relevant than simple baselines, without substantially sacrificing the quality of the generated summary. Expand
Weighted Global Normalization for Multiple Choice ReadingComprehension over Long Documents
TLDR
This work introduces a novel method for improving answer selection on long documents through weighted global normalization of predictions over portions of the documents through the use of a span prediction model adapted for answer selection. Expand
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