1 code implementation • 15 Apr 2024 • Kyubyung Chae, Jaepill Choi, Yohan Jo, Taesup Kim
We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglecting the details of the source text.
no code implementations • 3 Mar 2024 • Seo Hyun Kim, Keummin Ka, Yohan Jo, Seung-won Hwang, Dongha Lee, Jinyoung Yeo
To effectively construct memory, it is crucial to seamlessly connect past and present information, while also possessing the ability to forget obstructive information.
no code implementations • 19 Feb 2024 • Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim, Yohan Jo, Jaewoo Kang
Furthermore, we fine-tune a smaller LM using this dataset to align it with the retrievers' preferences as feedback.
no code implementations • 12 Jan 2024 • Taehee Kim, Yeongjae Cho, Heejun Shin, Yohan Jo, Dongmyung Shin
Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image.
1 code implementation • 31 Oct 2023 • Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
1 code implementation • 27 Oct 2023 • Dongjun Kang, Joonsuk Park, Yohan Jo, JinYeong Bak
Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing.
1 code implementation • 17 Oct 2023 • Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi
While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored.
1 code implementation • 1 Sep 2023 • Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, JungWoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.
1 code implementation • 12 May 2023 • Sunjun Kweon, Yeonsu Kwon, Seonhee Cho, Yohan Jo, Edward Choi
Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table.
1 code implementation • 11 May 2023 • Jiho Kim, Sungjin Park, Yeonsu Kwon, Yohan Jo, James Thorne, Edward Choi
KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability.
1 code implementation • 28 Feb 2023 • Sungbin Shin, Yohan Jo, Sungsoo Ahn, Namhoon Lee
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts.
1 code implementation • Findings (EMNLP) 2021 • Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, Eduard Hovy
Finding counterevidence to statements is key to many tasks, including counterargument generation.
1 code implementation • 17 May 2021 • Yohan Jo, Seojin Bang, Chris Reed, Eduard Hovy
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations.
1 code implementation • EMNLP 2020 • Yohan Jo, Jacky Visser, Chris Reed, Eduard Hovy
Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
1 code implementation • EMNLP 2020 • Yohan Jo, Seojin Bang, Emaad Manzoor, Eduard Hovy, Chris Reed
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation.
no code implementations • LREC 2020 • Yohan Jo, Elijah Mayfield, Chris Reed, Eduard Hovy
We introduce a corpus of the 2016 U. S. presidential debates and commentary, containing 4, 648 argumentative propositions annotated with fine-grained proposition types.
no code implementations • WS 2019 • Yohan Jo, Jacky Visser, Chris Reed, Eduard Hovy
Propositions are the basic units of an argument and the primary building blocks of most argument mining systems.
1 code implementation • WS 2019 • Xinru Yan, Aakanksha Naik, Yohan Jo, Carolyn Rose
We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures.
1 code implementation • NAACL 2018 • Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig
We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view.
no code implementations • IJCNLP 2017 • Keith Maki, Michael Yoder, Yohan Jo, Carolyn Ros{\'e}
In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article.
no code implementations • 8 Sep 2017 • Yohan Jo, Lisa Lee, Shruti Palaskar
There is a great need for technologies that can predict the mortality of patients in intensive care units with both high accuracy and accountability.
no code implementations • EMNLP 2017 • Yohan Jo, Michael Yoder, Hyeju Jang, Carolyn Ros{\'e}
We present an unsupervised model of dialogue act sequences in conversation.
no code implementations • 25 Mar 2017 • Yohan Jo
I started this work with the hope of generating a text synthesizer (like a musical synthesizer) that can imitate certain linguistic styles.