Search Results for author: Yohan Jo

Found 17 papers, 9 papers with code

Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table

1 code implementation12 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.

Open-Domain Question Answering

FactKG: Fact Verification via Reasoning on Knowledge Graphs

1 code implementation11 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.

Fact Verification Knowledge Graphs

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

1 code implementation28 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.


Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

1 code implementation17 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.

Argument Mining Representation Learning

Extracting Implicitly Asserted Propositions in Argumentation

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.

Argument Mining

Detecting Attackable Sentences in Arguments

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.

BIG-bench Machine Learning

Machine-Aided Annotation for Fine-Grained Proposition Types 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.

BIG-bench Machine Learning

A Cascade Model for Proposition Extraction in Argumentation

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.

Argument Mining Text Segmentation

Using Functional Schemas to Understand Social Media Narratives

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.

General Classification text-classification +1

Attentive Interaction Model: Modeling Changes in View in Argumentation

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.

Roles and Success in Wikipedia Talk Pages: Identifying Latent Patterns of Behavior

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.

Combining LSTM and Latent Topic Modeling for Mortality Prediction

no code implementations8 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.

Mortality Prediction

Simplifying the Bible and Wikipedia Using Statistical Machine Translation

no code implementations25 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.

Language Modelling Machine Translation +2

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