Search Results for author: Hwanhee Lee

Found 27 papers, 12 papers with code

Learning to Select Question-Relevant Relations for Visual Question Answering

no code implementations NAACL (maiworkshop) 2021 Jaewoong Lee, Heejoon Lee, Hwanhee Lee, Kyomin Jung

Previous existing visual question answering (VQA) systems commonly use graph neural networks(GNNs) to extract visual relationships such as semantic relations or spatial relations.

Graph Attention Question Answering +2

Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval

no code implementations17 Oct 2024 Ingeol Baek, Hwan Chang, Byeongjeong Kim, JiMin Lee, Hwanhee Lee

Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge.

Decision Making RAG +1

Crafting the Path: Robust Query Rewriting for Information Retrieval

no code implementations17 Jul 2024 Ingeol Baek, JiMin Lee, Joonho Yang, Hwanhee Lee

We demonstrate that our method is less dependent on the internal parameter knowledge of the model and generates queries with fewer factual inaccuracies.

Information Retrieval Retrieval

Conversational Query Reformulation with the Guidance of Retrieved Documents

no code implementations17 Jul 2024 Jeonghyun Park, Hwanhee Lee

Conversational search seeks to retrieve relevant passages for the given questions in conversational question answering.

Conversational Question Answering Conversational Search +2

Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis

no code implementations17 Jun 2024 Yonghyun Jun, Hwanhee Lee

Aspect-based sentiment analysis (ABSA) assesses sentiments towards specific aspects within texts, resulting in detailed sentiment tuples.

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

Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models

no code implementations7 Jun 2024 Gyutae Park, Seojin Hwang, Hwanhee Lee

We provide a future work direction to explore more effective few-shot learning strategies and to investigate the transfer learning capabilities of LLMs for cross-lingual summarization.

Few-Shot Learning Transfer Learning

AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence

no code implementations18 Apr 2024 Minbeom Kim, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung

Therefore, we've completed a benchmark encompassing daily life questions, diverse corresponding responses, and majority vote ranking to train our helpfulness metric.

Question Answering

FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document

1 code implementation17 Apr 2024 Joonho Yang, Seunghyun Yoon, Byeongjeong Kim, Hwanhee Lee

These atomic facts represent a more fine-grained unit of information, facilitating detailed understanding and interpretability of the summary's factual inconsistency.

Abstractive Text Summarization

KoCoSa: Korean Context-aware Sarcasm Detection Dataset

1 code implementation22 Feb 2024 Yumin Kim, Heejae Suh, Mingi Kim, Dongyeon Won, Hwanhee Lee

In this paper, we introduce a new dataset for the Korean dialogue sarcasm detection task, KoCoSa (Korean Context-aware Sarcasm Detection Dataset), which consists of 12. 8K daily Korean dialogues and the labels for this task on the last response.

Sarcasm Detection

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.

IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance

1 code implementation16 Nov 2023 Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, Kyomin Jung

To address these challenges, we propose Iterative Conversational Query Reformulation (IterCQR), a methodology that conducts query reformulation without relying on human rewrites.

Conversational Search Information Retrieval +1

Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources

no code implementations9 Nov 2023 Yerin Hwang, Yongil Kim, Hyunkyung Bae, Jeesoo Bang, Hwanhee Lee, Kyomin Jung

To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed.

Conversational Question Answering Re-Ranking

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 +3

Task-specific Compression for Multi-task Language Models using Attribution-based Pruning

no code implementations9 May 2022 Nakyeong Yang, Yunah Jang, Hwanhee Lee, Seohyeong Jung, Kyomin Jung

However, these language models utilize an unnecessarily large number of model parameters, even when used only for a specific task.

Natural Language Understanding

UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

1 code implementation ACL 2021 Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, Kyomin Jung

Also, we observe critical problems of the previous benchmark dataset (i. e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions.

Contrastive Learning Diversity +2

DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator

no code implementations1 Apr 2020 Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung

Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog.

Decoder Retrieval +1

Attentive Modality Hopping Mechanism for Speech Emotion Recognition

1 code implementation29 Nov 2019 Seunghyun Yoon, Subhadeep Dey, Hwanhee Lee, Kyomin Jung

In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.

Emotion Classification Multimodal Emotion Recognition +1

Improving Neural Question Generation using Answer Separation

1 code implementation7 Sep 2018 Yanghoon Kim, Hwanhee Lee, Joongbo Shin, Kyomin Jung

Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.

Question Generation Question-Generation

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