Search Results for author: Seokhwan Kim

Found 39 papers, 14 papers with code

PLACES: Prompting Language Models for Social Conversation Synthesis

1 code implementation7 Feb 2023 Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur

Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.

Conversational Response Generation

Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding

no code implementations25 Oct 2022 Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, Dilek Hakkani-Tur

Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.

Data Augmentation Dialogue Understanding +2

Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks

no code implementations SIGDIAL (ACL) 2022 Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur

Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.

Data Augmentation

Towards Textual Out-of-Domain Detection without In-Domain Labels

no code implementations22 Mar 2022 Di Jin, Shuyang Gao, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur

In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.

Contrastive Learning intent-classification +4

Training Conversational Agents with Generative Conversational Networks

no code implementations15 Oct 2021 Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur

Rich, open-domain textual data available on the web resulted in great advancements for language processing.

Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation

1 code implementation11 Oct 2021 Sashank Santhanam, Behnam Hedayatnia, Spandana Gella, Aishwarya Padmakumar, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur

We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.

Response Generation

Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems

no code implementations26 May 2020 Behnam Hedayatnia, Karthik Gopalakrishnan, Seokhwan Kim, Yang Liu, Mihail Eric, Dilek Hakkani-Tur

In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc.

Response Generation

Just Ask:An Interactive Learning Framework for Vision and Language Navigation

no code implementations2 Dec 2019 Ta-Chung Chi, Mihail Eric, Seokhwan Kim, Minmin Shen, Dilek Hakkani-Tur

We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.

Continual Learning Data Augmentation +2

Analyzing Sentence Fusion in Abstractive Summarization

no code implementations WS 2019 Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu

While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.

Abstractive Text Summarization Sentence Fusion

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