1 code implementation • ACL 2020 • Ting-Hao 'Kenneth' Huang, Chieh-Yang Huang, Chien-Kuang Cornelia Ding, Yen-Chia Hsu, C. Lee Giles
This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10, 966 English abstracts in the COVID-19 Open Research Dataset.
1 code implementation • ACL 2019 • Ting-Yao Hsu, Chieh-Yang Huang, Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task.
1 code implementation • 16 May 2023 • Hua Shen, Chieh-Yang Huang, Tongshuang Wu, Ting-Hao 'Kenneth' Huang
The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.
1 code implementation • NAACL 2021 • Chieh-Yang Huang, Ting-Hao 'Kenneth' Huang
In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e. g., 10, 100, or 200).
1 code implementation • EMNLP 2020 • Yun-Hsuan Jen, Chieh-Yang Huang, Mei-Hua Chen, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku
The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently.
1 code implementation • 17 Feb 2023 • Chieh-Yang Huang, Saniya Naphade, Kavya Laalasa Karanam, Ting-Hao 'Kenneth' Huang
Next, we conducted a preliminary user study using a story continuation task where AMT workers were given access to machine-generated story plots and asked to write a follow-up story.
1 code implementation • 7 Jun 2023 • Shreya Chandrasekhar, Chieh-Yang Huang, Ting-Hao 'Kenneth' Huang
In this study, we investigate the impact of different datasets on model performance for the crowd-annotated CODA-19 research aspect classification task.
no code implementations • 22 Jul 2017 • Chieh-Yang Huang, Tristan Labetoulle, Ting-Hao Kenneth Huang, Yi-Pei Chen, Hung-Chen Chen, Vallari Srivastava, Lun-Wei Ku
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
no code implementations • 9 Feb 2017 • Chieh-Yang Huang, Ting-Hao, Huang, Lun-Wei Ku
Instant messaging is one of the major channels of computer mediated communication.
no code implementations • EMNLP 2017 • Chieh-Yang Huang, Tristan Labetoulle, Ting-Hao Huang, Yi-Pei Chen, Hung-Chen Chen, Vallari Srivastava, Lun-Wei Ku
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.
no code implementations • COLING 2016 • Chieh-Yang Huang, Nicole Peinelt, Lun-Wei Ku
In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners.
no code implementations • WS 2019 • Chieh-Yang Huang, Yi-Ting Huang, Mei-Hua Chen, Lun-Wei Ku
In this study, students learn to differentiate the confusing words by reading the example sentences, and then choose the appropriate word(s) to complete the sentence translation task.
no code implementations • 29 Sep 2020 • Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter Language Modelling +5
no code implementations • EMNLP (WNUT) 2020 • Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter Language Modelling +4
no code implementations • FEVER (ACL) 2022 • Chieh-Yang Huang, Jinfeng Li, Nikita Bhutani, Alexander Whedon, Estevam Hruschka, Yoshi Suhara
To alleviate this scarcity problem, we develop an unsupervised method, ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities.
no code implementations • ACL 2022 • Anton Belyy, Chieh-Yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
Collecting data for conversational semantic parsing is a time-consuming and demanding process.
no code implementations • 23 Feb 2023 • Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi, Ani Nenkova, Sungchul Kim, Gromit Yeuk-Yin Chan, Eunyee Koh, Clyde Lee Giles, Ting-Hao 'Kenneth' Huang
Prior work often treated figure caption generation as a vision-to-language task.
no code implementations • 30 Mar 2023 • Shih-Hong Huang, Chieh-Yang Huang, Ya-Fang Lin, Ting-Hao 'Kenneth' Huang
The proliferation of automated conversational systems such as chatbots, spoken-dialogue systems, and smart speakers, has significantly impacted modern digital life.
no code implementations • 23 Oct 2023 • Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi, Sungchul Kim, C. Lee Giles, Ting-Hao K. Huang
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
no code implementations • 26 Mar 2024 • Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, C. Lee Giles, Ting-Hao K. Huang
Crafting effective captions for figures is important.