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 • 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 • 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 • 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.
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.
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.
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.
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 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.
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 • 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.
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
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.
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.
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.
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 • 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 • 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.