Papers by Debanjan Chaudhuri
A Retrospective Analysis of the Fake News Challenge Stance-Detection Task (C18-1)
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Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych
| Challenge: | 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. |
| Approach: | They propose a new evaluation metric favoring the majority class, which can be easily classified, and propose stacked LSTM models that perform on par with the best systems, but is superior in predicting minority classes. |
| Outcome: | The proposed evaluation metric favors the majority class, which can be easily classified, and overestimates the true discriminative power of the methods. |
RoMe: A Robust Metric for Evaluating Natural Language Generation (2022.acl-long)
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| Challenge: | Empirical results suggest that RoMe has a stronger correlation to human judgment over state-of-the-art metrics in evaluating system-generated sentences across several NLG tasks. |
| Approach: | They propose an automatic evaluation metric incorporating several core aspects of natural language understanding (language competence, syntactic and semantic variation). |
| Outcome: | The proposed evaluation metric is trained on language features such as semantic similarity combined with tree edit distance and grammatical acceptability, using a self-supervised neural network. |