Ukyo Honda


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Law Retrieval with Supervised Contrastive Learning Using the Hierarchical Structure of Law
Jungmin Choi | Ukyo Honda | Taro Watanabe | Hiroki Ouchi | Kentaro Inui
Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation


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Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
Ukyo Honda | Yoshitaka Ushiku | Atsushi Hashimoto | Taro Watanabe | Yuji Matsumoto
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images. In previous work, pseudo-captions, i.e., sentences that contain the detected object labels, were assigned to a given image. The focus of the previous work was on the alignment of input images and pseudo-captions at the sentence level. However, pseudo-captions contain many words that are irrelevant to a given image. In this work, we investigate the effect of removing mismatched words from image-sentence alignment to determine how they make this task difficult. We propose a simple gating mechanism that is trained to align image features with only the most reliable words in pseudo-captions: the detected object labels. The experimental results show that our proposed method outperforms the previous methods without introducing complex sentence-level learning objectives. Combined with the sentence-level alignment method of previous work, our method further improves its performance. These results confirm the importance of careful alignment in word-level details.


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Pruning Basic Elements for Better Automatic Evaluation of Summaries
Ukyo Honda | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.