Jennifer Brooks


GOT: Testing for Originality in Natural Language Generation
Jennifer Brooks | Abdou Youssef
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach and a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.


Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks
Jennifer Brooks | Abdou Youssef
Proceedings of the Second Workshop on Figurative Language Processing

In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: “nouns”, “verbs”, “adverbs/adjectives”, or “other”. The models were combined into voting pools and the voting pools were combined using the logical “OR” operator.