• Publications
  • Influence
StereoSet: Measuring stereotypical bias in pretrained language models
TLDR
A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or Asians are bad drivers. Expand
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Identifying Depression on Twitter
  • Moin Nadeem
  • Computer Science, Mathematics
  • ArXiv
  • 25 July 2016
TLDR
Social media has recently emerged as a premier method to disseminate information online. Expand
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FAKTA: An Automatic End-to-End Fact Checking System
TLDR
We present FAKTA which is a unified framework that integrates various components of a fact-checking process to not only predict the factuality of given claims, but also provide evidence at the document and sentence level to explain its predictions. Expand
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Neural Multi-Task Learning for Stance Prediction
TLDR
We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction and investigate the effectiveness of different NLP tasks for stance prediction. Expand
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
TLDR
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Expand
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Automating Network Error Detection using Long-Short Term Memory Networks
TLDR
We investigate the current flaws with identifying network-related errors, and examine how K-Means and Long-Short Term Memory Networks solve these problems. Expand
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A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation
TLDR
This work studies the widely adopted ancestral sampling algorithms for auto-regressive language models, which is not widely studied in the literature. Expand
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Neural Educational Recommendation Engine (NERE)
TLDR
We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. Expand
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Context-Aware Systems for Sequential Item Recommendation
TLDR
We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. Expand