Goran Topić

Also published as: Goran Topic


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Audio Commentary System for Real-Time Racing Game Play
Tatsuya Ishigaki | Goran Topić | Yumi Hamazono | Ichiro Kobayashi | Yusuke Miyao | Hiroya Takamura
Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations

Live commentaries are essential for enhancing spectators’ enjoyment and understanding during sports events or e-sports streams. We introduce a live audio commentator system designed specifically for a racing game, driven by the high demand in the e-sports field. While a player is playing a racing game, our system tracks real-time user play data including speed and steer rotations, and generates commentary to accompany the live stream. Human evaluation suggested that generated commentary enhances enjoyment and understanding of races compared to streams without commentary. Incorporating additional modules to improve diversity and detect irregular events, such as course-outs and collisions, further increases the preference for the output commentaries.

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Pretraining Language- and Domain-Specific BERT on Automatically Translated Text
Tatsuya Ishigaki | Yui Uehara | Goran Topić | Hiroya Takamura
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Domain-specific pretrained language models such as SciBERT are effective for various tasks involving text in specific domains. However, pretraining BERT requires a large-scale language resource, which is not necessarily available in fine-grained domains, especially in non-English languages. In this study, we focus on a setting with no available domain-specific text for pretraining. To this end, we propose a simple framework that trains a BERT on text in the target language automatically translated from a resource-rich language, e.g., English. In this paper, we particularly focus on the materials science domain in Japanese. Our experiments pertain to the task of entity and relation extraction for this domain and language. The experiments demonstrate that the various models pretrained on translated texts consistently perform better than the general BERT in terms of F1 scores although the domain-specific BERTs do not use any human-authored domain-specific text. These results imply that BERTs for various low-resource domains can be successfully trained on texts automatically translated from resource-rich languages.

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Disease Network Constructor: a Pathway Extraction and Visualization
Mohammad Golam Sohrab | Khoa Duong | Goran Topić | Masami Ikeda | Nozomi Nagano | Yayoi Natsume-Kitatani | Masakata Kuroda | Mari Itoh | Hiroya Takamura
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

We present Disease Network Constructor (DNC), a system that extracts and visualizes a disease network, in which nodes are entities such as diseases, proteins, and genes, and edges represent regulation relation. We focused on the disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis (IPF). The front-end web-base user interface of DNC includes two-dimensional (2D) and 3D visualizations of the constructed disease network. The back-end system of DNC includes several natural language processing (NLP) techniques to process biomedical text including BERT-based tokenization on the basis of Bidirectional Encoder Representations from Transformers (BERT), flat and nested named entity recognition (NER), candidate generation and candidate ranking for entity linking (EL) or, relation extraction (RE), and event extraction (EE) tasks. We evaluated the end-to-end EL and end-to-end nested EE systems to determine the DNC’s back-endimplementation performance. To the best of our knowledge, this is the first attempt that addresses neural NER, EL, RE, and EE tasks in an end-to-end manner that constructs a path-way visualization from events, which we name Disease Network Constructor. The demonstration video can be accessed from https://youtu.be/rFhWwAgcXE8. We release an online system for end users and the source code is available at https://github.com/aistairc/PRISM-APIs/.


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Open-domain Video Commentary Generation
Edison Marrese-Taylor | Yumi Hamazono | Tatsuya Ishigaki | Goran Topić | Yusuke Miyao | Ichiro Kobayashi | Hiroya Takamura
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Live commentary plays an important role in sports broadcasts and video games, making spectators more excited and immersed. In this context, though approaches for automatically generating such commentary have been proposed in the past, they have been generally concerned with specific fields, where it is possible to leverage domain-specific information. In light of this, we propose the task of generating video commentary in an open-domain fashion. We detail the construction of a new large-scale dataset of transcribed commentary aligned with videos containing various human actions in a variety of domains, and propose approaches based on well-known neural architectures to tackle the task. To understand the strengths and limitations of current approaches, we present an in-depth empirical study based on our data. Our results suggest clear trade-offs between textual and visual inputs for the models and highlight the importance of relying on external knowledge in this open-domain setting, resulting in a set of robust baselines for our task.

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BiomedCurator: Data Curation for Biomedical Literature
Mohammad Golam Sohrab | Khoa N.A. Duong | Ikeda Masami | Goran Topić | Yayoi Natsume-Kitatani | Masakata Kuroda | Mari Nogami Itoh | Hiroya Takamura
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

We present BiomedCurator1, a web application that extracts the structured data from scientific articles in PubMed and ClinicalTrials.gov. BiomedCurator uses state-of-the-art natural language processing techniques to fill the fields pre-selected by domain experts in the relevant biomedical area. The BiomedCurator web application includes: text generation based model for relation extraction, entity detection and recognition, text classification model for extracting several fields, information retrieval from external knowledge base to retrieve IDs, and a pattern-based extraction approach that can extract several fields using regular expressions over the PubMed and ClinicalTrials.gov datasets. Evaluation results show that different approaches of BiomedCurator web application system are effective for automatic data curation in the biomedical domain.


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Generating Racing Game Commentary from Vision, Language, and Structured Data
Tatsuya Ishigaki | Goran Topic | Yumi Hamazono | Hiroshi Noji | Ichiro Kobayashi | Yusuke Miyao | Hiroya Takamura
Proceedings of the 14th International Conference on Natural Language Generation

We propose the task of automatically generating commentaries for races in a motor racing game, from vision, structured numerical, and textual data. Commentaries provide information to support spectators in understanding events in races. Commentary generation models need to interpret the race situation and generate the correct content at the right moment. We divide the task into two subtasks: utterance timing identification and utterance generation. Because existing datasets do not have such alignments of data in multiple modalities, this setting has not been explored in depth. In this study, we introduce a new large-scale dataset that contains aligned video data, structured numerical data, and transcribed commentaries that consist of 129,226 utterances in 1,389 races in a game. Our analysis reveals that the characteristics of commentaries change over time or from viewpoints. Our experiments on the subtasks show that it is still challenging for a state-of-the-art vision encoder to capture useful information from videos to generate accurate commentaries. We make the dataset and baseline implementation publicly available for further research.


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BENNERD: A Neural Named Entity Linking System for COVID-19
Mohammad Golam Sohrab | Khoa Duong | Makoto Miwa | Goran Topić | Ikeda Masami | Takamura Hiroya
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a biomedical entity linking (EL) system BENNERD that detects named enti- ties in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research. BEN- NERD mainly covers biomedical domain, es- pecially new entity types (e.g., coronavirus, vi- ral proteins, immune responses) by address- ing CORD-NER dataset. It includes several NLP tools to process biomedical texts includ- ing tokenization, flat and nested entity recog- nition, and candidate generation and rank- ing for EL that have been pre-trained using the CORD-NER corpus. To the best of our knowledge, this is the first attempt that ad- dresses NER and EL on COVID-19-related entities, such as COVID-19 virus, potential vaccines, and spreading mechanism, that may benefit research on COVID-19. We release an online system to enable real-time entity annotation with linking for end users. We also release the manually annotated test set and CORD-NERD dataset for leveraging EL task. The BENNERD system is available at https://aistairc.github.io/BENNERD/.

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An empirical analysis of existing systems and datasets toward general simple question answering
Namgi Han | Goran Topic | Hiroshi Noji | Hiroya Takamura | Yusuke Miyao
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a model on a single dataset, and recent studies suggest that SimpleQuestions, the most popular and largest dataset, is nearly solved under this setting. However, this common setting does not evaluate the robustness of the systems outside of the distribution of the used training data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering. We discuss a possible future direction toward this goal.


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CroVeWA: Crosslingual Vector-Based Writing Assistance
Hubert Soyer | Goran Topić | Pontus Stenetorp | Akiko Aizawa
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations


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Significance of Bridging Real-world Documents and NLP Technologies
Tadayoshi Hara | Goran Topić | Yusuke Miyao | Akiko Aizawa
Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT


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Sense Disambiguation: From Natural Language Words to Mathematical Terms
Minh-Quoc Nghiem | Giovanni Yoko Kristianto | Goran Topić | Akiko Aizawa
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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brat: a Web-based Tool for NLP-Assisted Text Annotation
Pontus Stenetorp | Sampo Pyysalo | Goran Topić | Tomoko Ohta | Sophia Ananiadou | Jun’ichi Tsujii
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics


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BioNLP Shared Task 2011: Supporting Resources
Pontus Stenetorp | Goran Topić | Sampo Pyysalo | Tomoko Ohta | Jin-Dong Kim | Jun’ichi Tsujii
Proceedings of BioNLP Shared Task 2011 Workshop