Jong C. Park

Also published as: Jong Park


2022

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Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words
Jung-Ho Kim | Eui Jun Hwang | Sukmin Cho | Du Hui Lee | Jong Park
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Sign language production (SLP) is the process of generating sign language videos from spoken language expressions. Since sign languages are highly under-resourced, existing vision-based SLP approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems and thus generate low-quality translations. To address these problems, we introduce an avatar-based SLP system composed of a sign language translation (SLT) model and an avatar animation generation module. Our Transformer-based SLT model utilizes two additional strategies to resolve these problems: named entity transformation to reduce OOV tokens and context vector generation using a pretrained language model (e.g., BERT) to reliably train the decoder. Our system is validated on a new Korean-Korean Sign Language (KSL) dataset of weather forecasts and emergency announcements. Our SLT model achieves an 8.77 higher BLEU-4 score and a 4.57 higher ROUGE-L score over those of our baseline model. In a user evaluation, 93.48% of named entities were successfully identified by participants, demonstrating marked improvement on OOV issues.

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ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
Huije Lee | Young Ju Na | Hoyun Song | Jisu Shin | Jong Park
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned with our dataset shows a significantly improved performance in strategy-controlled sentence generation.

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GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages
Fitsum Gaim | Wonsuk Yang | Jong C. Park
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.

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Query Generation with External Knowledge for Dense Retrieval
Sukmin Cho | Soyeong Jeong | Wonsuk Yang | Jong Park
Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures

Dense retrieval aims at searching for the most relevant documents to the given query by encoding texts in the embedding space, requiring a large amount of query-document pairs to train. Since manually constructing such training data is challenging, recent work has proposed to generate synthetic queries from documents and use them to train a dense retriever. However, compared to the manually composed queries, synthetic queries do not generally ask for implicit information, therefore leading to a degraded retrieval performance. In this work, we propose Query Generation with External Knowledge (QGEK), a novel method for generating queries with external information related to the corresponding document. Specifically, we convert a query into a triplet-based template form to accommodate external information and transmit it to a pre-trained language model (PLM). We validate QGEK on both in-domain and out-domain dense retrieval settings. The dense retriever with the queries requiring implicit information is found to make good performance improvement. Also, such queries are similar to manually composed queries, confirmed by both human evaluation and unique & non-unique words distribution.

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Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
Soyeong Jeong | Jinheon Baek | Sukmin Cho | Sung Ju Hwang | Jong Park
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense representation space, have gained considerable attention for their remarkable success. Yet, dense models require a vast amount of labeled training data for notable performance, whereas it is often challenging to acquire query-document pairs annotated by humans. To tackle this problem, we propose a simple but effective Document Augmentation for dense Retrieval (DAR) framework, which augments the representations of documents with their interpolation and perturbation. We validate the performance of DAR on retrieval tasks with two benchmark datasets, showing that the proposed DAR significantly outperforms relevant baselines on the dense retrieval of both the labeled and unlabeled documents.

2021

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A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from Reddit
Hoyun Song | Soo Hyun Ryu | Huije Lee | Jong Park
Proceedings of the 25th Conference on Computational Natural Language Learning

As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection. However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity. In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts. Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale. We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights. The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.

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Optimizing Domain Specificity of Transformer-based Language Models for Extractive Summarization of Financial News Articles in Korean
Huije Lee | Wonsuk Yang | Chaehun Park | Hoyun Song | Eugene Jang | Jong C. Park
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
Soyeong Jeong | Jinheon Baek | ChaeHun Park | Jong Park
Proceedings of the Second Workshop on Scholarly Document Processing

One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are lexically different but semantically similar. While recent work has proposed to expand the queries or documents by enriching their representations with additional relevant terms to address this challenge, they usually require a large volume of query-document pairs to train an expansion model. In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training. For generating sentences, we further stochastically perturb their embeddings to generate more diverse sentences for document expansion. We validate our framework on two standard IR benchmark datasets. The results show that our framework significantly outperforms relevant expansion baselines for IR.

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Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Heng Ji | Jong C. Park | Rui Xia
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

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Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model
ChaeHun Park | Eugene Jang | Wonsuk Yang | Jong Park
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment. To address this problem, researchers have investigated the possibility of assessing response quality without using a set of known correct responses. RUBER demonstrated that an automatic response evaluation model could be made using unsupervised learning for the next-utterance prediction (NUP) task. For the unsupervised learning of such model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. We find, from our experiments on English datasets, that using the negative samples generated by our method alongside random negative samples can increase the model’s correlation with human evaluations. The process of generating such negative samples is automated and does not rely on human annotation.

2019

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Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials
Wonsuk Yang | Seungwon Yoon | Ada Carpenter | Jong Park
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.

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Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
ChaeHun Park | Wonsuk Yang | Jong Park
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda

Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.

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Computer Assisted Annotation of Tension Development in TED Talks through Crowdsourcing
Seungwon Yoon | Wonsuk Yang | Jong Park
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

We propose a method of machine-assisted annotation for the identification of tension development, annotating whether the tension is increasing, decreasing, or staying unchanged. We use a neural network based prediction model, whose predicted results are given to the annotators as initial values for the options that they are asked to choose. By presenting such initial values to the annotators, the annotation task becomes an evaluation task where the annotators inspect whether or not the predicted results are correct. To demonstrate the effectiveness of our method, we performed the annotation task in both in-house and crowdsourced environments. For the crowdsourced environment, we compared the annotation results with and without our method of machine-assisted annotation. We find that the results with our method showed a higher agreement to the gold standard than those without, though our method had little effect at reducing the time for annotation. Our codes for the experiment are made publicly available.

2018

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Feature Attention Network: Interpretable Depression Detection from Social Media
Hoyun Song | Jinseon You | Jin-Woo Chung | Jong C. Park
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2017

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Extraction of Gene-Environment Interaction from the Biomedical Literature
Jinseon You | Jin-Woo Chung | Wonsuk Yang | Jong C. Park
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.

2015

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Corpus annotation with a linguistic analysis of the associations between event mentions and spatial expressions
Jin-Woo Chung | Jinseon You | Jong C. Park
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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CoMAGD: Annotation of Gene-Depression Relations
Rize Jin | Jinseon You | Jin-Woo Chung | Hee-Jin Lee | Maria Wolters | Jong Park
Proceedings of BioNLP 15

2013

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Proceedings of the Sixth International Joint Conference on Natural Language Processing
Ruslan Mitkov | Jong C. Park
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Parsing Dependency Paths to Identify Event-Argument Relations
Seung-Cheol Baek | Jong Park
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Product Name Classification for Product Instance Distinction
Hye-Jin Min | Jong C. Park
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation

2011

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Detecting and Blocking False Sentiment Propagation
Hye-Jin Min | Jong C. Park
Proceedings of 5th International Joint Conference on Natural Language Processing

2009

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Toward finer-grained sentiment identification in product reviews through linguistic and ontological analyses
Hye-Jin Min | Jong C. Park
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2007

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Analysis of Indirect Uses of Interrogative Sentences Carrying Anger
Hye-Jin Min | Jong C. Park
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation

2005

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From Text to Sign Language: Exploiting the Spatial and Motioning Dimension
Ji-Won Choi | Hee-Jin Lee | Jong C. Park
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation

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Vowel Sound Disambiguation for Intelligible Korean Speech Synthesis
Ho-Joon Lee | Jong C. Park
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation

2004

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BioAR: Anaphora Resolution for Relating Protein Names to Proteome Database Entries
Jung-Jae Kim | Jong C. Park
Proceedings of the Conference on Reference Resolution and Its Applications

2002

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Natural Language Interpretations for Heterogeneous Database Access
Hodong Lee | Jong C. Park
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Automatic Augmentation of Translation Dictionary with Database Terminologies In Multilingual Query Interpretation
Hodong Lee | Jong C. Park
Proceedings of the ACL 2001 Workshop on Human Language Technology and Knowledge Management

2000

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Informed Parsing for Coordination with Combinatory Categorial Grammar
Jong C. Park | Hyung Joon Cho
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

1999

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Lexical selection with a target language monolingual corpus and an MRD
Hyun Ah Lee | Jong C. Park | Gil Chang Kim
Proceedings of the 8th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1997

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An English Grammar Checker as a Writing Aid for Students of English as a Second Language
Jong C. Park | Martha Palmer | Clay Washburn
Fifth Conference on Applied Natural Language Processing: Descriptions of System Demonstrations and Videos

1995

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Quantifier Scope and Constituency
Jong C. Park
33rd Annual Meeting of the Association for Computational Linguistics

1992

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A Unification-Based Semantic Interpretation for Coordinate Constructs
Jong C. Park
30th Annual Meeting of the Association for Computational Linguistics