Byron Galbraith


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Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks
Bhanu Pratap | Daniel Shank | Oladipo Ositelu | Byron Galbraith
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes our approach to SemEval-2018 Task 7 – given an entity-tagged text from the ACL Anthology corpus, identify and classify pairs of entities that have one of six possible semantic relationships. Our model consists of a convolutional neural network leveraging pre-trained word embeddings, unlabeled ACL-abstracts, and multiple window sizes to automatically learn useful features from entity-tagged sentences. We also experiment with a hybrid loss function, a combination of cross-entropy loss and ranking loss, to boost the separation in classification scores. Lastly, we include WordNet-based features to further improve the performance of our model. Our best model achieves an F1(macro) score of 74.2 and 84.8 on subtasks 1.1 and 1.2, respectively.


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Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection
Byron Galbraith | Bhanu Pratap | Daniel Shank
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.