Cynthia Rudin


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Metaphor Detection Using Contextual Word Embeddings From Transformers
Jerry Liu | Nathan O’Hara | Alexander Rubin | Rachel Draelos | Cynthia Rudin
Proceedings of the Second Workshop on Figurative Language Processing

The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0% on VUA AllPOS, 73.0% on VUA Verbs, 66.9% on TOEFL AllPOS, and 69.7% on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.

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A Transformer Approach to Contextual Sarcasm Detection in Twitter
Hunter Gregory | Steven Li | Pouya Mohammadi | Natalie Tarn | Rachel Draelos | Cynthia Rudin
Proceedings of the Second Workshop on Figurative Language Processing

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online. One of the most difficult, yet important tones to detect is sarcasm. In the past, LSTM and transformer architecture models have been used to tackle this problem. We attempt to expand upon this research, implementing LSTM, GRU, and transformer models, and exploring new methods to classify sarcasm in Twitter posts. Among these, the most successful were transformer models, most notably BERT. While we attempted a few other models described in this paper, our most successful model was an ensemble of transformer models including BERT, RoBERTa, XLNet, RoBERTa-large, and ALBERT. This research was performed in conjunction with the sarcasm detection shared task section in the Second Workshop on Figurative Language Processing, co-located with ACL 2020.


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Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking
Ryan Roth | Owen Rambow | Nizar Habash | Mona Diab | Cynthia Rudin
Proceedings of ACL-08: HLT, Short Papers


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Re-Ranking Algorithms for Name Tagging
Heng Ji | Cynthia Rudin | Ralph Grishman
Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing