Hwanhee Lee


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ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT
Hwanhee Lee | Seunghyun Yoon | Franck Dernoncourt | Doo Soon Kim | Trung Bui | Kyomin Jung
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems

In this paper, we propose an evaluation metric for image captioning systems using both image and text information. Unlike the previous methods that rely on textual representations in evaluating the caption, our approach uses visiolinguistic representations. The proposed method generates image-conditioned embeddings for each token using ViLBERT from both generated and reference texts. Then, these contextual embeddings from each of the two sentence-pair are compared to compute the similarity score. Experimental results on three benchmark datasets show that our method correlates significantly better with human judgments than all existing metrics.


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AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
Yanghoon Kim | Hwanhee Lee | Kyomin Jung
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human’s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.