most citedMind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization

12 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models

Heerin Yang, Sseung-won Hwang, Jungmin So

Although pre-trained language models show good performance on various natural language processing tasks, they often rely on non-causal features and patterns to determine the outcom…

cs.CV2024★ 1 cited

SMCL: Saliency Masked Contrastive Learning for Long-tailed Recognition

Sanglee Park, Seung-won Hwang, Jungmin So

Real-world data often follow a long-tailed distribution with a high imbalance in the number of samples between classes. The problem with training from imbalanced data is that some…

cs.CL2022★ 1 cited

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

Minju Kim, Chaehyeong Kim, Yongho Song +2

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…

cs.CL2022

Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption

Garam Lee, Minsoo Kim, Jai Hyun Park +2

Embeddings, which compress information in raw text into semantics-preserving low-dimensional vectors, have been widely adopted for their efficacy. However, recent research has show…

cs.CL2022★ 12 cited

Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization

Seungone Kim, Se June Joo, Hyungjoo Chae +3

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

cs.AI2022

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

Yu Jin Kim, Beong-woo Kwak, Youngwook Kim +3

Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to…

cs.CL2022

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

Minju Kim, Beong-woo Kwak, Youngwook Kim +3

This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly dep…