Joonsuk Park


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Plug-and-Play Adaptation for Continuously-updated QA
Kyungjae Lee | Wookje Han | Seung-won Hwang | Hwaran Lee | Joonsuk Park | Sang-Woo Lee
Findings of the Association for Computational Linguistics: ACL 2022

Language models (LMs) have shown great potential as implicit knowledge bases (KBs). And for their practical use, knowledge in LMs need to be updated periodically. However, existing tasks to assess LMs’ efficacy as KBs do not adequately consider multiple large-scale updates. 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. We then present LMs with plug-in modules that effectively handle the updates. Experiments conducted on zsRE QA and NQ datasets show that our method outperforms existing approaches. We find that our method is 4x more effective in terms of updates/forgets ratio, compared to a fine-tuning baseline.

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Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
Hwanhee Lee | Kang Min Yoo | Joonsuk Park | Hwaran Lee | Kyomin Jung
Findings of the Association for Computational Linguistics: NAACL 2022

Despite the recent advances in abstractive summarization systems, it is still difficult to determine whether a generated summary is factual consistent with the source text. To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries. Luckily, the former is readily available as reference summaries in existing summarization datasets. However, generating the latter remains a challenge, as they need to be factually inconsistent, yet closely relevant to the source text to be effective. In this paper, we propose to generate factually inconsistent summaries using source texts and reference summaries with key information masked. Experiments on seven benchmark datasets demonstrate that factual consistency classifiers trained on summaries generated using our method generally outperform existing models and show a competitive correlation with human judgments. We also analyze the characteristics of the summaries generated using our method. We will release the pre-trained model and the code at

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Argument Mining for Review Helpfulness Prediction
Zaiqian Chen | Daniel Verdi do Amarante | Jenna Donaldson | Yohan Jo | Joonsuk Park
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features—such as the structure of arguments and the types of underlying elementary units—have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM2) corpus—a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.


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Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate
Muhammad Mahad Afzal Bhatti | Ahsan Suheer Ahmad | Joonsuk Park
Proceedings of the 8th Workshop on Argument Mining

Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale. Such information can be invaluable to policy makers, marketers and social scientists, to name a few. However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argument — both a claim and a premise. 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. To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets. We first present a new dataset of 24,100 tweets containing hashtag #StandWithPP or #DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITHOUT REASON, and NO EXPLICIT SUPPORT. We then train classifiers to determine the types of tweets, achieving the best performance of 71% F1. Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.

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Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning
Aalok Sathe | Joonsuk Park
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask—given a document and a (sentence) claim, determine whether the document supports or refutes the claim—only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.


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Automated Fact-Checking of Claims from Wikipedia
Aalok Sathe | Salar Ather | Tuan Manh Le | Nathan Perry | Joonsuk Park
Proceedings of the Twelfth Language Resources and Evaluation Conference

Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. 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. To this end, we present WikiFactCheck-English, a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations, as well as 34k+ manually written claims that are refuted by the evidence documents. This is the largest fact checking dataset consisting of real claims and evidence to date; it will allow the development of fact checking systems that can better process claims and evidence in the real world. We also show that for the NLI subtask, a logistic regression system trained using existing and novel features achieves peak accuracy of 68%, providing a competitive baseline for future work. Also, a decomposable attention model trained on SNLI significantly underperforms the models trained on this dataset, suggesting that models trained on manually generated data may not be sufficiently generalizable or suitable for fact checking real-world claims.


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A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments
Joonsuk Park | Claire Cardie
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Argument Mining with Structured SVMs and RNNs
Vlad Niculae | Joonsuk Park | Claire Cardie
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.


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A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues
Barbara Konat | John Lawrence | Joonsuk Park | Katarzyna Budzynska | Chris Reed
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- & con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.


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Automatic Identification of Rhetorical Questions
Shohini Bhattasali | Jeremy Cytryn | Elana Feldman | Joonsuk Park
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments
Joonsuk Park | Arzoo Katiyar | Bishan Yang
Proceedings of the 2nd Workshop on Argumentation Mining


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Identifying Appropriate Support for Propositions in Online User Comments
Joonsuk Park | Claire Cardie
Proceedings of the First Workshop on Argumentation Mining


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Improving Implicit Discourse Relation Recognition Through Feature Set Optimization
Joonsuk Park | Claire Cardie
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue