Search Results for author: Joonsuk Park

Found 23 papers, 8 papers with code

Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate

no code implementations EMNLP (ArgMining) 2021 Muhammad Mahad Afzal Bhatti, Ahsan Suheer Ahmad, Joonsuk Park

In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest.

Argument Mining text-classification +1

Argument Quality Assessment in the Age of Instruction-Following Large Language Models

no code implementations24 Mar 2024 Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio, Anne Lauscher, Joonsuk Park, Eva Maria Vecchi, Serena Villata, Timon Ziegenbein

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like.

Decision Making Instruction Following

LifeTox: Unveiling Implicit Toxicity in Life Advice

no code implementations16 Nov 2023 Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung

As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial.

On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model

no code implementations14 Nov 2023 Nohil Park, Joonsuk Park, Kang Min Yoo, Sungroh Yoon

An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models.

NER POS

From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models

1 code implementation27 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.

Marketing Question Answering

Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

1 code implementation23 Oct 2023 Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park, Jaewoo Kang

To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer.

Open-Domain Question Answering Retrieval

KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application

1 code implementation28 May 2023 Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Gunhee Kim, Jung-Woo Ha

Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.

Language Modelling Large Language Model +1

Asking Clarification Questions to Handle Ambiguity in Open-Domain QA

1 code implementation23 May 2023 Dongryeol Lee, Segwang Kim, Minwoo Lee, Hwanhee Lee, Joonsuk Park, Sang-Woo Lee, Kyomin Jung

We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.

Open-Domain Question Answering

Critic-Guided Decoding for Controlled Text Generation

no code implementations21 Dec 2022 Minbeom Kim, Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung

In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.

Language Modelling reinforcement-learning +2

ClaimDiff: Comparing and Contrasting Claims on Contentious Issues

1 code implementation24 May 2022 Miyoung Ko, Ingyu Seong, Hwaran Lee, Joonsuk Park, Minsuk Chang, Minjoon Seo

With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence.

Fact Verification Misinformation

Plug-and-Play Adaptation for Continuously-updated QA

no code implementations Findings (ACL) 2022 Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee

To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.

Automated Fact-Checking of Claims from Wikipedia

no code implementations LREC 2020 Aalok Sathe, Salar Ather, Tuan Manh Le, Nathan Perry, Joonsuk Park

However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet.

Fact Checking

Argument Mining with Structured SVMs and RNNs

1 code implementation ACL 2017 Vlad Niculae, Joonsuk Park, Claire Cardie

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure.

Argument Mining General Classification

A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues

no code implementations LREC 2016 Barbara Konat, John Lawrence, Joonsuk Park, Katarzyna Budzynska, Chris Reed

Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens.

Argument Mining

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