Search Results for author: Seung-won Hwang

Found 50 papers, 17 papers with code

Debiasing Event Understanding for Visual Commonsense Tasks

no code implementations Findings (ACL) 2022 Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, Bei Liu

We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction. We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.

Structure-Augmented Keyphrase Generation

1 code implementation EMNLP 2021 Jihyuk Kim, Myeongho Jeong, Seungtaek Choi, Seung-won Hwang

The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text.

Keyphrase Generation

ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

1 code implementation24 Feb 2024 Soyoung Yoon, Eunbi Lee, Jiyeon Kim, Yireun Kim, Hyeongu Yun, Seung-won Hwang

We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time.


Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

no code implementations13 Nov 2023 Seungjun Moon, Hyungjoo Chae, Yongho Song, Taeyoon Kwon, Dongjin Kang, Kai Tzu-iunn Ong, Seung-won Hwang, Jinyoung Yeo

Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing.

Program Synthesis

On Monotonic Aggregation for Open-domain QA

1 code implementation8 Aug 2023 Sang-eun Han, Yeonseok Jeong, Seung-won Hwang, Kyungjae Lee

Our experiments show that our framework not only ensures monotonicity, but also outperforms state-of-the-art multi-source QA methods on Natural Questions.

Language Modelling Natural Questions +4

When to Read Documents or QA History: On Unified and Selective Open-domain QA

no code implementations7 Jun 2023 Kyungjae Lee, Sang-eun Han, Seung-won Hwang, Moontae Lee

This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources.

Natural Questions Open-Domain Question Answering +2

Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering

no code implementations6 Apr 2023 Yongho Song, Dahyun Lee, Myungha Jang, Seung-won Hwang, Kyungjae Lee, Dongha Lee, Jinyeong Yeo

The long-standing goal of dense retrievers in abtractive open-domain question answering (ODQA) tasks is to learn to capture evidence passages among relevant passages for any given query, such that the reader produce factually correct outputs from evidence passages.

Contrastive Learning counterfactual +4

BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets

1 code implementation23 Oct 2022 Minju Kim, Chaehyeong Kim, Yongho Song, Seung-won Hwang, Jinyoung Yeo

To build open-domain chatbots that are able to use diverse communicative skills, we propose a novel framework BotsTalk, where multiple agents grounded to the specific target skills participate in a conversation to automatically annotate multi-skill dialogues.

Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption

no code implementations NAACL 2022 Garam Lee, Minsoo Kim, Jai Hyun Park, Seung-won Hwang, Jung Hee Cheon

Embeddings, which compress information in raw text into semantics-preserving low-dimensional vectors, have been widely adopted for their efficacy.

Privacy Preserving text-classification +1

Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization

1 code implementation COLING 2022 Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo

In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.

Abstractive Dialogue Summarization Multi-Task Learning +1

Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning

no code implementations NAACL 2022 Yu Jin Kim, Beong-woo Kwak, Youngwook Kim, Reinald Kim Amplayo, Seung-won Hwang, Jinyoung Yeo

Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework.

Knowledge Graphs Transfer Learning

Collective Relevance Labeling for Passage Retrieval

1 code implementation NAACL 2022 Jihyuk Kim, Minsoo Kim, Seung-won Hwang

Deep learning for Information Retrieval (IR) requires a large amount of high-quality query-document relevance labels, but such labels are inherently sparse.

Knowledge Distillation Passage Retrieval +1

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.

ReACC: A Retrieval-Augmented Code Completion Framework

1 code implementation ACL 2022 Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, Alexey Svyatkovskiy

Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development.

Code Completion Language Modelling +1

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

no code implementations11 Feb 2022 Minju Kim, Beong-woo Kwak, Youngwook Kim, Hong-in Lee, Seung-won Hwang, Jinyoung Yeo

This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents.


GRPE: Relative Positional Encoding for Graph Transformer

1 code implementation30 Jan 2022 Wonpyo Park, WoongGi Chang, Donggeon Lee, Juntae Kim, Seung-won Hwang

The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction.

Graph Classification Graph Regression +3

TrustAL: Trustworthy Active Learning using Knowledge Distillation

no code implementations26 Jan 2022 Beong-woo Kwak, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, Jinyoung Yeo

A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency.

Active Learning Knowledge Distillation

Query Generation for Multimodal Documents

no code implementations EACL 2021 Kyungho Kim, Kyungjae Lee, Seung-won Hwang, Young-In Song, SeungWook Lee

This paper studies the problem of generatinglikely queries for multimodal documents withimages.


Retrieval-Augmented Controllable Review Generation

no code implementations COLING 2020 Jihyeok Kim, Seungtaek Choi, Reinald Kim Amplayo, Seung-won Hwang

We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes.

Attribute Descriptive +3

Meta-path Free Semi-supervised Learning for Heterogeneous Networks

no code implementations18 Oct 2020 Shin-woo Park, Byung Jun Bae, Jinyoung Yeo, Seung-won Hwang

Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification.

Node Classification Representation Learning

SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0

no code implementations LREC 2020 Gyeongbok Lee, Seung-won Hwang, Hyunsouk Cho

Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2. 0 which contains such type of questions.

Machine Reading Comprehension

Evaluating Research Novelty Detection: Counterfactual Approaches

no code implementations WS 2019 Reinald Kim Amplayo, Seung-won Hwang, Min Song

We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models.

counterfactual feature selection +2

NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions

no code implementations IJCNLP 2019 Fuxiang Chen, Seung-won Hwang, Jaegul Choo, Jung-Woo Ha, Sunghun Kim

Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL.


Text Length Adaptation in Sentiment Classification

1 code implementation18 Sep 2019 Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang

We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms.

Classification General Classification +4

Soft Representation Learning for Sparse Transfer

no code implementations ACL 2019 Haeju Park, Jinyoung Yeo, Gengyu Wang, Seung-won Hwang

Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances.

Multi-Task Learning Representation Learning

KBQA: Learning Question Answering over QA Corpora and Knowledge Bases

no code implementations6 Mar 2019 Wanyun Cui, Yanghua Xiao, Haixun Wang, Yangqiu Song, Seung-won Hwang, Wei Wang

Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions.

Question Answering

Categorical Metadata Representation for Customized Text Classification

2 code implementations TACL 2019 Jihyeok Kim, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, Seung-won Hwang

The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e. g., using user/product information for sentiment classification.

Ranked #4 on Sentiment Analysis on User and product information (Yelp 2013 (Acc) metric)

General Classification Sentence +5

QADiver: Interactive Framework for Diagnosing QA Models

no code implementations1 Dec 2018 Gyeongbok Lee, Sungdong Kim, Seung-won Hwang

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset.

Question Answering

AutoSense Model for Word Sense Induction

1 code implementation22 Nov 2018 Reinald Kim Amplayo, Seung-won Hwang, Min Song

Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word.

Word Sense Induction

Adversarial TableQA: Attention Supervision for Question Answering on Tables

no code implementations18 Oct 2018 Minseok Cho, Reinald Kim Amplayo, Seung-won Hwang, Jonghyuck Park

The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table.

Question Answering

Mining Cross-Cultural Differences and Similarities in Social Media

no code implementations ACL 2018 Bill Yuchen Lin, Frank F. Xu, Kenny Zhu, Seung-won Hwang

Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media.

Machine Translation Natural Language Understanding +2

Cold-Start Aware User and Product Attention for Sentiment Classification

1 code implementation ACL 2018 Reinald Kim Amplayo, Jihyeok Kim, Sua Sung, Seung-won Hwang

The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited.

Classification General Classification +2

Entity Commonsense Representation for Neural Abstractive Summarization

1 code implementation NAACL 2018 Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang

To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary.

Abstractive Text Summarization Entity Linking +1

Aspect Sentiment Model for Micro Reviews

1 code implementation14 Jun 2018 Reinald Kim Amplayo, Seung-won Hwang

This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3

Verb Pattern: A Probabilistic Semantic Representation on Verbs

no code implementations20 Oct 2017 Wanyun Cui, Xiyou Zhou, Hangyu Lin, Yanghua Xiao, Haixun Wang, Seung-won Hwang, Wei Wang

In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb.


Entity Suggestion by Example using a Conceptual Taxonomy

no code implementations29 Nov 2015 Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang

This paper provides a query processing method based on the relevance models between entity sets and concepts.

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