Search Results for author: Dongyeop Kang

Found 42 papers, 26 papers with code

Visualizing Cross‐Lingual Discourse Relations in Multilingual TED Corpora

1 code implementation CODI 2021 Zae Myung Kim, Vassilina Nikoulina, Dongyeop Kang, Didier Schwab, Laurent Besacier

This paper presents an interactive data dashboard that provides users with an overview of the preservation of discourse relations among 28 language pairs.

infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information

1 code implementation30 May 2023 Jaehyung Kim, Yekyung Kim, Karin de Langis, Jinwoo Shin, Dongyeop Kang

However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy.

Diffusion Models in NLP: A Survey

no code implementations24 May 2023 Hao Zou, Zae Myung Kim, Dongyeop Kang

In NLP, diffusion models have been used in a variety of applications, such as natural language generation, sentiment analysis, topic modeling, and machine translation.

Few-Shot Learning Machine Translation +2

Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions

no code implementations24 May 2023 London Lowmanstone, Ruyuan Wan, Risako Owan, Jaehyung Kim, Dongyeop Kang

Thus, we propose using imputation methods to restore the opinions of all annotators for all examples, creating a dataset that does not leave out any annotator's view.


Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction

no code implementations24 May 2023 Anna Martin-Boyle, Andrew Head, Kyle Lo, Risham Sidhu, Marti A. Hearst, Dongyeop Kang

We also introduce a new definition extraction method that masks mathematical symbols, creates a copy of each sentence for each symbol, specifies a target symbol, and predicts its corresponding definition spans using slot filling.

Definition Extraction slot-filling +1

Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer

no code implementations24 May 2023 Debarati Das, David Ma, Dongyeop Kang

This paper explores the impact of training data input diversity on the quality of the generated text from the multi-style transfer model.

Style Transfer Text Style Transfer

"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures

no code implementations23 May 2023 Zae Myung Kim, David E. Taylor, Dongyeop Kang

Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances.


CoEdIT: Text Editing by Task-Specific Instruction Tuning

1 code implementation17 May 2023 Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

Text editing or revision is an essential function of the human writing process.

Language Modelling

Decoding the End-to-end Writing Trajectory in Scholarly Manuscripts

1 code implementation31 Mar 2023 Ryan Koo, Anna Martin, Linghe Wang, Dongyeop Kang

We also provide ManuScript, an original dataset annotated with a simplified version of our taxonomy to show writer actions and the intentions behind them.

Text Generation

Cluster-Guided Label Generation in Extreme Multi-Label Classification

1 code implementation17 Feb 2023 Taehee Jung, Joo-Kyung Kim, Sungjin Lee, Dongyeop Kang

For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels.

Classification Extreme Multi-Label Classification

Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information

no code implementations12 Jan 2023 Ruyuan Wan, Jaehyung Kim, Dongyeop Kang

Particularly, we extract disagreement labels from the annotators' voting histories in the five subjective datasets, and then fine-tune language models to predict annotators' disagreement.

Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets

no code implementations20 Dec 2022 Risako Owan, Maria Gini, Dongyeop Kang

We observe that both frameworks have similar inter-annotator agreements, despite having different numbers of sense types (8 for Quirk and 3 for Palmer).

Natural Language Understanding

A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models

1 code implementation19 Dec 2022 Karin de Langis, Dongyeop Kang

We develop a variety of methods to derive style saliency scores over text using the collected eye dataset.

Few-Shot Learning

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks

1 code implementation2 Dec 2022 Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.

Grammatical Error Correction Sentence Fusion +2

RedPen: Region- and Reason-Annotated Dataset of Unnatural Speech

no code implementations26 Oct 2022 Kyumin Park, Keon Lee, Daeyoung Kim, Dongyeop Kang

We present a novel speech dataset, RedPen, with human annotations on unnatural speech regions and their corresponding reasons.

Speech Synthesis

StyLEx: Explaining Style Using Human Lexical Annotations

1 code implementation14 Oct 2022 Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal, Lyle Ungar, Dongyeop Kang

Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).

Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision

1 code implementation In2Writing (ACL) 2022 Wanyu Du, Zae Myung Kim, Vipul Raheja, Dhruv Kumar, Dongyeop Kang

Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants.

Understanding Out-of-distribution: A Perspective of Data Dynamics

no code implementations NeurIPS Workshop ICBINB 2021 Dyah Adila, Dongyeop Kang

Despite machine learning models' success in Natural Language Processing (NLP) tasks, predictions from these models frequently fail on out-of-distribution (OOD) samples.

BIG-bench Machine Learning

What Makes Better Augmentation Strategies? Augment Difficult but Not too Different

no code implementations ICLR 2022 Jaehyung Kim, Dongyeop Kang, Sungsoo Ahn, Jinwoo Shin

Remarkably, our method is more effective on the challenging low-data and class-imbalanced regimes, and the learned augmentation policy is well-transferable to the different tasks and models.

Data Augmentation Semantic Similarity +3

Zero-shot Natural Language Video Localization

1 code implementation ICCV 2021 Jinwoo Nam, Daechul Ahn, Dongyeop Kang, Seong Jong Ha, Jonghyun Choi

Understanding videos to localize moments with natural language often requires large expensive annotated video regions paired with language queries.

Image Captioning

Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding

1 code implementation ACL 2021 Dongyeop Kang, Eduard Hovy

This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation.

Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions

1 code implementation EMNLP (sdp) 2020 Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst

Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.

Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task

no code implementations EMNLP 2020 Dongyeop Kang, Eduard Hovy

To address that, we propose a self-supervised text planner SSPlanner that predicts what to say first (content prediction), then guides the pretrained language model (surface realization) using the predicted content.

Language Modelling

INSPIRED: Toward Sociable Recommendation Dialog Systems

1 code implementation EMNLP 2020 Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu

To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.

Movie Recommendation

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

1 code implementation29 Sep 2020 Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst

We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols.

Posterior Calibrated Training on Sentence Classification Tasks

1 code implementation ACL 2020 Taehee Jung, Dongyeop Kang, Hua Cheng, Lucas Mentch, Thomas Schaaf

Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities. We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives.

Classification General Classification +1

Style is NOT a single variable: Case Studies for Cross-Style Language Understanding

2 code implementations9 Nov 2019 Dongyeop Kang, Eduard Hovy

This paper provides the benchmark corpus (xSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation.

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

1 code implementation IJCNLP 2019 Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston

These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.

Recommendation Systems

Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs

1 code implementation IJCNLP 2019 Dongyeop Kang, Hiroaki Hayashi, Alan W. black, Eduard Hovy

In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e. g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves.

Language Modelling

Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

1 code implementation IJCNLP 2019 Taehee Jung, Dongyeop Kang, Lucas Mentch, Eduard Hovy

We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes.

News Summarization

Bridging Knowledge Gaps in Neural Entailment via Symbolic Models

no code implementations EMNLP 2018 Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Peter Clark

We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts.

Natural Language Inference

AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples

1 code implementation ACL 2018 Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard Hovy

We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it.

Natural Language Inference

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

1 code implementation NAACL 2018 Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz

In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.

Detecting and Explaining Causes From Text For a Time Series Event

1 code implementation EMNLP 2017 Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, Eduard Hovy

Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.

Time Series Analysis

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