Search Results for author: Shinsuke Mori

Found 42 papers, 2 papers with code

Analysis of the Effect of Dependency Information on Predicate-Argument Structure Analysis and Zero Anaphora Resolution

no code implementations31 May 2017 Koichiro Yoshino, Shinsuke Mori, Satoshi Nakamura

This paper investigates and analyzes the effect of dependency information on predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR) for Japanese, and shows that a straightforward approach of PASA and ZAR works effectively even if dependency information was not available.

Dependency Parsing POS

Procedural Text Generation from an Execution Video

no code implementations IJCNLP 2017 Atsushi Ushiku, Hayato Hashimoto, Atsushi Hashimoto, Shinsuke Mori

In this paper, we focus on procedure execution videos, in which a human makes or repairs something and propose a method for generating procedural texts from them.

Object Recognition Text Generation +1

A Japanese Word Dependency Corpus

no code implementations LREC 2014 Shinsuke Mori, Hideki Ogura, Tetsuro Sasada

Dictionary example sentences have pronunciation annotation and cover basic vocabulary in Japanese with English sentence equivalent.

Dependency Parsing Machine Translation +2

Language Resource Addition: Dictionary or Corpus?

no code implementations LREC 2014 Shinsuke Mori, Graham Neubig

The experimental results showed that the annotated sentence addition to the training corpus is better than the entries addition to the dictionary.

Active Learning Domain Adaptation +4

Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation

no code implementations31 May 2019 Tianyu Zhao, Shinsuke Mori, Tatsuya Kawahara

Various encoder-decoder models have been applied to response generation in open-domain dialogs, but a majority of conventional models directly learn a mapping from lexical input to lexical output without explicitly modeling intermediate representations.

Response Generation Sentence

Language Resource Addition Strategies for Raw Text Parsing

no code implementations LREC 2016 Atsushi Ushiku, Tetsuro Sasada, Shinsuke Mori

In Japanese, the raw text parsing is divided into three steps: word segmentation, part-of-speech tagging, and dependency parsing.

Dependency Parsing Part-Of-Speech Tagging +1

Wikification for Scriptio Continua

no code implementations LREC 2016 Yugo Murawaki, Shinsuke Mori

To address this problem, we propose to define a separate task that directly links given texts to an external resource, that is, wikification in the case of Wikipedia.

Segmentation

Universal Dependencies for Japanese

no code implementations LREC 2016 Takaaki Tanaka, Yusuke Miyao, Masayuki Asahara, Sumire Uematsu, Hiroshi Kanayama, Shinsuke Mori, Yuji Matsumoto

We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper.

Procedural Text Generation from a Photo Sequence

no code implementations WS 2019 Taichi Nishimura, Atsushi Hashimoto, Shinsuke Mori

Multimedia procedural texts, such as instructions and manuals with pictures, support people to share how-to knowledge.

Text Generation

A Contract Corpus for Recognizing Rights and Obligations

no code implementations LREC 2020 Ruka Funaki, Yusuke Nagata, Kohei Suenaga, Shinsuke Mori

Therefore, a language-processing system that can present information concerning rights and obligations found within a given contract document would help a contracting party to make better decisions.

Visual Grounding Annotation of Recipe Flow Graph

no code implementations LREC 2020 Taichi Nishimura, Suzushi Tomori, Hayato Hashimoto, Atsushi Hashimoto, Yoko Yamakata, Jun Harashima, Yoshitaka Ushiku, Shinsuke Mori

Visual grounding is provided as bounding boxes to image sequences of recipes, and each bounding box is linked to an element of the workflow.

Visual Grounding

Annotating Event Appearance for Japanese Chess Commentary Corpus

no code implementations LREC 2020 Hirotaka Kameko, Shinsuke Mori

In this paper, we propose {``}Event Appearance{''} labels that show the relationship between events mentioned in texts and those happening in the real world.

Natural Language Queries Relation +1

English Recipe Flow Graph Corpus

no code implementations LREC 2020 Yoko Yamakata, Shinsuke Mori, John Carroll

For r-NE tagging we train a deep neural network NER tool; to compute flow graphs we train a dependency-style parsing procedure which we apply to the entire sequence of r-NEs in a recipe. In evaluations, our systems achieve 71. 1 to 87. 5 F1, demonstrating that our annotation scheme is learnable.

NER

Neural Text Generation with Artificial Negative Examples

no code implementations28 Dec 2020 Keisuke Shirai, Kazuma Hashimoto, Akiko Eriguchi, Takashi Ninomiya, Shinsuke Mori

In this paper, we propose to suppress an arbitrary type of errors by training the text generation model in a reinforcement learning framework, where we use a trainable reward function that is capable of discriminating between references and sentences containing the targeted type of errors.

Image Captioning Machine Translation +2

Recipe Generation from Unsegmented Cooking Videos

no code implementations21 Sep 2022 Taichi Nishimura, Atsushi Hashimoto, Yoshitaka Ushiku, Hirotaka Kameko, Shinsuke Mori

However, unlike DVC, in recipe generation, recipe story awareness is crucial, and a model should extract an appropriate number of events in the correct order and generate accurate sentences based on them.

Dense Video Captioning Recipe Generation +1

Towards Flow Graph Prediction of Open-Domain Procedural Texts

no code implementations31 May 2023 Keisuke Shirai, Hirotaka Kameko, Shinsuke Mori

In this work, we propose a framework based on the recipe FG for flow graph prediction of open-domain procedural texts.

Domain Adaptation Reading Comprehension

Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks

no code implementations25 Mar 2024 Keyaki Ohno, Hirotaka Kameko, Keisuke Shirai, Taichi Nishimura, Shinsuke Mori

By utilizing hyperlinks, we can accurately assign location expressions with coordinates even with ambiguous location expressions in the texts.

Text-driven Affordance Learning from Egocentric Vision

no code implementations3 Apr 2024 Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura, Shinsuke Mori

The key idea of our approach is employing textual instruction, targeting various affordances for a wide range of objects.

Referring Expression Referring Expression Comprehension

BioVL-QR: Egocentric Biochemical Video-and-Language Dataset Using Micro QR Codes

no code implementations4 Apr 2024 Taichi Nishimura, Koki Yamamoto, Yuto Haneji, Keiya Kajimura, Chihiro Nishiwaki, Eriko Daikoku, Natsuko Okuda, Fumihito Ono, Hirotaka Kameko, Shinsuke Mori

From our preliminary study, we found that detecting objects only using Micro QR Codes is still difficult because the researchers manipulate objects, causing blur and occlusion frequently.

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