Yasuhide Miura


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Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Yasuhide Miura | Yuhao Zhang | Emily Tsai | Curtis Langlotz | Dan Jurafsky
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F1 score of a clinical information extraction performance by +22.1 (Delta +63.9%). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.


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Distinctive Slogan Generation with Reconstruction
Shotaro Misawa | Yasuhide Miura | Tomoki Taniguchi | Tomoko Ohkuma
Proceedings of Workshop on Natural Language Processing in E-Commerce

E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers’ attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation–based reconstruction model with refer–attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.

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Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations
Ryuji Kano | Yasuhide Miura | Tomoki Taniguchi | Tomoko Ohkuma
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We propose Implicit Quote Extractor, an end-to-end unsupervised extractive neural summarization model for conversational texts. When we reply to posts, quotes are used to highlight important part of texts. We aim to extract quoted sentences as summaries. Most replies do not explicitly include quotes, so it is difficult to use quotes as supervision. However, even if it is not explicitly shown, replies always refer to certain parts of texts; we call them implicit quotes. Implicit Quote Extractor aims to extract implicit quotes as summaries. The training task of the model is to predict whether a reply candidate is a true reply to a post. For prediction, the model has to choose a few sentences from the post. To predict accurately, the model learns to extract sentences that replies frequently refer to. We evaluate our model on two email datasets and one social media dataset, and confirm that our model is useful for extractive summarization. We further discuss two topics; one is whether quote extraction is an important factor for summarization, and the other is whether our model can capture salient sentences that conventional methods cannot.


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Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning
Toru Nishino | Shotaro Misawa | Ryuji Kano | Tomoki Taniguchi | Yasuhide Miura | Tomoko Ohkuma
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The automated generation of information indicating the characteristics of articles such as headlines, key phrases, summaries and categories helps writers to alleviate their workload. Previous research has tackled these tasks using neural abstractive summarization and classification methods. However, the outputs may be inconsistent if they are generated individually. The purpose of our study is to generate multiple outputs consistently. We introduce a multi-task learning model with a shared encoder and multiple decoders for each task. We propose a novel loss function called hierarchical consistency loss to maintain consistency among the attention weights of the decoders. To evaluate the consistency, we employ a human evaluation. The results show that our model generates more consistent headlines, key phrases and categories. In addition, our model outperforms the baseline model on the ROUGE scores, and generates more adequate and fluent headlines.

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Relation Prediction for Unseen-Entities Using Entity-Word Graphs
Yuki Tagawa | Motoki Taniguchi | Yasuhide Miura | Tomoki Taniguchi | Tomoko Ohkuma | Takayuki Yamamoto | Keiichi Nemoto
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.


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Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning
Motoki Taniguchi | Yasuhide Miura | Tomoko Ohkuma
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance.

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Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification
Motoki Taniguchi | Tomoki Taniguchi | Takumi Takahashi | Yasuhide Miura | Tomoko Ohkuma
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

We describe here our system and results on the FEVER shared task. We prepared a pipeline system which composes of a document selection, a sentence retrieval, and a recognizing textual entailment (RTE) components. A simple entity linking approach with text match is used as the document selection component, this component identifies relevant documents for a given claim by using mentioned entities as clues. The sentence retrieval component selects relevant sentences as candidate evidence from the documents based on TF-IDF. Finally, the RTE component selects evidence sentences by ranking the sentences and classifies the claim simultaneously. The experimental results show that our system achieved the FEVER score of 0.4016 and outperformed the official baseline system.

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Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
Ryuji Kano | Yasuhide Miura | Motoki Taniguchi | Yan-Ying Chen | Francine Chen | Tomoko Ohkuma
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.

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Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts
Yasuhide Miura | Ryuji Kano | Motoki Taniguchi | Tomoki Taniguchi | Shotaro Misawa | Tomoko Ohkuma
Proceedings of the 27th International Conference on Computational Linguistics

We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.


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Using Social Networks to Improve Language Variety Identification with Neural Networks
Yasuhide Miura | Tomoki Taniguchi | Motoki Taniguchi | Shotaro Misawa | Tomoko Ohkuma
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a hierarchical neural network model for language variety identification that integrates information from a social network. Recently, language variety identification has enjoyed heightened popularity as an advanced task of language identification. The proposed model uses additional texts from a social network to improve language variety identification from two perspectives. First, they are used to introduce the effects of homophily. Secondly, they are used as expanded training data for shared layers of the proposed model. By introducing information from social networks, the model improved its accuracy by 1.67-5.56. Compared to state-of-the-art baselines, these improved performances are better in English and comparable in Spanish. Furthermore, we analyzed the cases of Portuguese and Arabic when the model showed weak performances, and found that the effect of homophily is likely to be weak due to sparsity and noises compared to languages with the strong performances.

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Character-based Bidirectional LSTM-CRF with words and characters for Japanese Named Entity Recognition
Shotaro Misawa | Motoki Taniguchi | Yasuhide Miura | Tomoko Ohkuma
Proceedings of the First Workshop on Subword and Character Level Models in NLP

Recently, neural models have shown superior performance over conventional models in NER tasks. These models use CNN to extract sub-word information along with RNN to predict a tag for each word. However, these models have been tested almost entirely on English texts. It remains unclear whether they perform similarly in other languages. We worked on Japanese NER using neural models and discovered two obstacles of the state-of-the-art model. First, CNN is unsuitable for extracting Japanese sub-word information. Secondly, a model predicting a tag for each word cannot extract an entity when a part of a word composes an entity. The contributions of this work are (1) verifying the effectiveness of the state-of-the-art NER model for Japanese, (2) proposing a neural model for predicting a tag for each character using word and character information. Experimentally obtained results demonstrate that our model outperforms the state-of-the-art neural English NER model in Japanese.

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Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction
Yasuhide Miura | Motoki Taniguchi | Tomoki Taniguchi | Tomoko Ohkuma
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.


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A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter
Yasuhide Miura | Motoki Taniguchi | Tomoki Taniguchi | Tomoko Ohkuma
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

This paper describes a model that we submitted to W-NUT 2016 Shared task #1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91% and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55% and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.

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Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets
Tuan Anh Le | David Moeljadi | Yasuhide Miura | Tomoko Ohkuma
Proceedings of the 12th Workshop on Asian Language Resources (ALR12)

This paper describes our attempt to build a sentiment analysis system for Indonesian tweets. With this system, we can study and identify sentiments and opinions in a text or document computationally. We used four thousand manually labeled tweets collected in February and March 2016 to build the model. Because of the variety of content in tweets, we analyze tweets into eight groups in total, including pos(itive), neg(ative), and neu(tral). Finally, we obtained 73.2% accuracy with Long Short Term Memory (LSTM) without normalizer.


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TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data
Yasuhide Miura | Shigeyuki Sakaki | Keigo Hattori | Tomoko Ohkuma
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Twitter User Gender Inference Using Combined Analysis of Text and Image Processing
Shigeyuki Sakaki | Yasuhide Miura | Xiaojun Ma | Keigo Hattori | Tomoko Ohkuma
Proceedings of the Third Workshop on Vision and Language


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Topic Modeling with Sentiment Clues and Relaxed Labeling Schema
Yasuhide Miura | Keigo Hattori | Tomoko Ohkuma | Hiroshi Masuichi
Proceedings of the 3rd Workshop on Sentiment Analysis where AI meets Psychology

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Incorporating Knowledge Resources to Enhance Medical Information Extraction
Yasuhide Miura | Tomoko Ohkuma | Hiroshi Masuichi | Emiko Yamada Shinohara | Eiji Aramaki | Kazuhiko Ohe
The First Workshop on Natural Language Processing for Medical and Healthcare Fields


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Adverse-Effect Relations Extraction from Massive Clinical Records
Yasuhide Miura | Eiji Aramaki | Tomoko Ohkuma | Masatsugu Tonoike | Daigo Sugihara | Hiroshi Masuichi | Kazuhiko Ohe
Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010)


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TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification
Eiji Aramaki | Yasuhide Miura | Masatsugu Tonoike | Tomoko Ohkuma | Hiroshi Mashuichi | Kazuhiko Ohe
Proceedings of the BioNLP 2009 Workshop