Papers by Moin Nadeem

4 papers
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation (2020.aacl-main)

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Challenge: Existing sampling algorithms for auto-regressive language models have similar performance . entropy reduction, order preservation and slope preservation are common properties of existing methods .
Approach: They investigate the quality-diversity trade-off between ancestral sampling algorithms for auto-regressive language models.
Outcome: The proposed methods have similar performance to existing methods for open-ended language generation.
Neural Multi-Task Learning for Stance Prediction (D19-66)

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Challenge: Existing models for fact checking are limited in size due to limited data available . stance detection is a key component of fact checking for journalists and news agencies .
Approach: They propose to use textual information from existing datasets to improve stance prediction.
Outcome: The proposed model outperforms state-of-the-art systems on a public benchmark dataset by 6.0 and 14.4 points in weighting.
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)

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Challenge: Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability .
Approach: They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet .
Outcome: The proposed model shows strong stereotypical biases in gender, profession, race, and religion domains.
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)

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Challenge: Existing studies have investigated individual components of fact checking process but none offer such a capability.
Approach: They propose a framework that integrates various components of a fact-checking process.
Outcome: The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions.

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