cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey

no code implementations • 26 May 2016 • Hirokatsu Kataoka, Yudai Miyashita, Tomoaki Yamabe, Soma Shirakabe, Shin'ichi Sato, Hironori Hoshino, Ryo Kato, Kaori Abe, Takaaki Imanari, Naomichi Kobayashi, Shinichiro Morita, Akio Nakamura

The "cvpaper. challenge" is a group composed of members from AIST, Tokyo Denki Univ.

Changing Fashion Cultures

3 code implementations • 23 Mar 2017 • Kaori Abe, Teppei Suzuki, Shunya Ueta, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka

The paper presents a novel concept that analyzes and visualizes worldwide fashion trends.

Cultural Vocal Bursts Intensity Prediction Time Series +1

Collaborative Descriptors: Convolutional Maps for Preprocessing

no code implementations • 10 May 2017 • Hirokatsu Kataoka, Kaori Abe, Akio Nakamura, Yutaka Satoh

The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features.

Object Recognition

cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey

2 code implementations • 20 Jul 2017 • Hirokatsu Kataoka, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, Hiroya Yatsuyanagi, Shinya Maruyama, Ryosuke Takasawa, Masataka Fuchida, Yudai Miyashita, Kazushige Okayasu, Yuta Matsuzaki

The paper gives futuristic challenges disscussed in the cvpaper. challenge.

Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

1 code implementation • ACL 2020 • Takuma Kato, Kaori Abe, Hiroki Ouchi, Shumpei Miyawaki, Jun Suzuki, Kentaro Inui

In general, the labels used in sequence labeling consist of different types of elements.

named-entity-recognition Named Entity Recognition +1

PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents

1 code implementation • COLING 2020 • Ryo Fujii, Masato Mita, Kaori Abe, Kazuaki Hanawa, Makoto Morishita, Jun Suzuki, Kentaro Inui

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.

Machine Translation NMT +1

Chat Translation Error Detection for Assisting Cross-lingual Communications

1 code implementation • 2 Aug 2023 • Yunmeng Li, Jun Suzuki, Makoto Morishita, Kaori Abe, Ryoko Tokuhisa, Ana Brassard, Kentaro Inui

In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of current machine chat translation methods.

Translation

Multi-dialect Neural Machine Translation and Dialectometry

no code implementations • PACLIC 2018 • Kaori Abe, Yuichiroh Matsubayashi, Naoaki Okazaki, Kentaro Inui

Machine Translation Translation

Topicalization in Language Models: A Case Study on Japanese

no code implementations • COLING 2022 • Riki Fujihara, Tatsuki Kuribayashi, Kaori Abe, Ryoko Tokuhisa, Kentaro Inui

Humans use different wordings depending on the context to facilitate efficient communication.

Position Sentence +1

Overview of the 8th Workshop on Asian Translation

no code implementations • ACL (WAT) 2021 • Toshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Shohei Higashiyama, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Shantipriya Parida, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Sadao Kurohashi

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021).

Translation

Overview of the 9th Workshop on Asian Translation

no code implementations • WAT 2022 • Toshiaki Nakazawa, Hideya Mino, Isao Goto, Raj Dabre, Shohei Higashiyama, Shantipriya Parida, Anoop Kunchukuttan, Makoto Morishita, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Sadao Kurohashi

This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022).

Translation