Tatsuya Harada


2022

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Plug-and-Play Controller for Story Completion: A Pilot Study toward Emotion-aware Story Writing Assistance
Yusuke Mori | Hiroaki Yamane | Ryohei Shimizu | Tatsuya Harada
Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022)

Emotions are essential for storytelling and narrative generation, and as such, the relationship between stories and emotions has been extensively studied. The authors of this paper, including a professional novelist, have examined the use of natural language processing to address the problems of novelists from the perspective of practical creative writing. In particular, the story completion task, which requires understanding the existing unfinished context, was studied from the perspective of creative support for human writers, to generate appropriate content to complete the unfinished parts. It was found that unsupervised pre-trained large neural models of the sequence-to-sequence type are useful for this task. Furthermore, based on the plug-and-play module for controllable text generation using GPT-2, an additional module was implemented to consider emotions. Although this is a preliminary study, and the results leave room for improvement before incorporating the model into a practical system, this effort is an important step in complementing the emotional trajectory of the story.

2020

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Finding and Generating a Missing Part for Story Completion
Yusuke Mori | Hiroaki Yamane | Yusuke Mukuta | Tatsuya Harada
Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Creating a story is difficult. Professional writers often experience a writer’s block. Thus, providing automatic support to writers is crucial but also challenging. Recently, in the field of generating and understanding stories, story completion (SC) has been proposed as a method for generating missing parts of an incomplete story. Despite this method’s usefulness in providing creative support, its applicability is currently limited because it requires the user to have prior knowledge of the missing part of a story. Writers do not always know which part of their writing is flawed. To overcome this problem, we propose a novel approach called “missing position prediction (MPP).” Given an incomplete story, we aim to predict the position of the missing part. We also propose a novel method for MPP and SC. We first conduct an experiment focusing on MPP, and our analysis shows that highly accurate predictions can be obtained when the missing part of a story is the beginning or the end. This suggests that if a story has a specific beginning or end, they play significant roles. We conduct an experiment on SC using MPP, and our proposed method demonstrates promising results.

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Unsupervised Keyword Extraction for Full-Sentence VQA
Kohei Uehara | Tatsuya Harada
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.

2019

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Toward a Better Story End: Collecting Human Evaluation with Reasons
Yusuke Mori | Hiroaki Yamane | Yusuke Mukuta | Tatsuya Harada
Proceedings of the 12th International Conference on Natural Language Generation

Creativity is an essential element of human nature used for many activities, such as telling a story. Based on human creativity, researchers have attempted to teach a computer to generate stories automatically or support this creative process. In this study, we undertake the task of story ending generation. This is a relatively new task, in which the last sentence of a given incomplete story is automatically generated. This is challenging because, in order to predict an appropriate ending, the generation method should comprehend the context of events. Despite the importance of this task, no clear evaluation metric has been established thus far; hence, it has remained an open problem. Therefore, we study the various elements involved in evaluating an automatic method for generating story endings. First, we introduce a baseline hierarchical sequence-to-sequence method for story ending generation. Then, we conduct a pairwise comparison against human-written endings, in which annotators choose the preferable ending. In addition to a quantitative evaluation, we conduct a qualitative evaluation by asking annotators to specify the reason for their choice. From the collected reasons, we discuss what elements the evaluation should focus on, to thereby propose effective metrics for the task.

2012

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Dialog System Using Real-Time Crowdsourcing and Twitter Large-Scale Corpus
Fumihiro Bessho | Tatsuya Harada | Yasuo Kuniyoshi
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue