Hwanhee Lee


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Learning to Select Question-Relevant Relations for Visual Question Answering
Jaewoong Lee | Heejoon Lee | Hwanhee Lee | Kyomin Jung
Proceedings of the Third Workshop on Multimodal Artificial Intelligence

Previous existing visual question answering (VQA) systems commonly use graph neural networks(GNNs) to extract visual relationships such as semantic relations or spatial relations. However, studies that use GNNs typically ignore the importance of each relation and simply concatenate outputs from multiple relation encoders. In this paper, we propose a novel layer architecture that fuses multiple visual relations through an attention mechanism to address this issue. Specifically, we develop a model that uses question embedding and joint embedding of the encoders to obtain dynamic attention weights with regard to the type of questions. Using the learnable attention weights, the proposed model can efficiently use the necessary visual relation features for a given question. Experimental results on the VQA 2.0 dataset demonstrate that the proposed model outperforms existing graph attention network-based architectures. Additionally, we visualize the attention weight and show that the proposed model assigns a higher weight to relations that are more relevant to the question.

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KPQA : A Metric for Generative Question Answering Using Keyphrase WeightsKPQA: A Metric for Generative Question Answering Using Keyphrase Weights
Hwanhee Lee | Seunghyun Yoon | Franck Dernoncourt | Doo Soon Kim | Trung Bui | Joongbo Shin | Kyomin Jung
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In the automatic evaluation of generative question answering (GenQA) systems, it is difficult to assess the correctness of generated answers due to the free-form of the answer. Especially, widely used n-gram similarity metrics often fail to discriminate the incorrect answers since they equally consider all of the tokens. To alleviate this problem, we propose KPQA metric, a new metric for evaluating the correctness of GenQA. Specifically, our new metric assigns different weights to each token via keyphrase prediction, thereby judging whether a generated answer sentence captures the key meaning of the reference answer. To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets. Using our human-evaluation datasets, we show that our proposed metric has a significantly higher correlation with human judgments than existing metrics in various datasets. Code for KPQA-metric will be available at https://github.com/hwanheelee1993/KPQA.


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ViLBERTScore : Evaluating Image Caption Using Vision-and-Language BERTViLBERTScore: 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.