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Found 6 papers, 1 papers with code
An Emotion-based Korean Multimodal Empathetic Dialogue System
We propose a Korean multimodal dialogue system targeting emotion-based empathetic dialogues because most research in this field has been conducted in a few languages such as English and Japanese and in certain circumstances.
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems
Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge.
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning
Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation.
Automatic Gloss-level Data Augmentation for Sign Language Translation
In this study, we present three methods of augmenting sign language text modality data, comprising 3, 052 Gloss-level Korean Sign Language (GKSL) and Word-level Korean Language (WKL) sentence pairs.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
We present an analytic study on the language of news media in the context of political fact-checking and fake news detection.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news {--} satire, hoaxes, clickbait and propaganda.
