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Yoonna Jang

7 accepted papers

2026

Interpretable Debiasing of Vision-Language Models for Social Fairness

CVPR 2026

The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended forms of social bias. Current debiasing approaches focus on mitigating surface-level bias signals through post-hoc learning or test-time algorithms, wh

Cited by 0SourceScholar
2023

Explore the Way: Exploring Reasoning Path by Bridging Entities for Effective Cross-Document Relation Extraction

EMNLP 2023short findings

Cross-document relation extraction (CodRED) task aims to infer the relation between two entities mentioned in different documents within a reasoning path. Previous studies have concentrated on merely capturing implicit relations between the entities. However, humans usually utilize explicit informat…

Cited by 0SourceScholar
2023

Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations

EMNLP 2023long main

Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue or unfaithful to a given source. Especially in the task of knowledge grounded conversation, the models are required to ge…

Cited by 0SourcecodeScholar
2022

A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation

NAACL 2022findings

Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets. However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositio…

2022

Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

AAAI 2022technical

Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the u…

2022

You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge

EMNLP 2022finding

To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of usin…

2020

I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning

COLING 2020main

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation…