Papers by Seolhwa Lee
Priming Ancient Korean Neural Machine Translation (2022.lrec-1)
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| Challenge: | Recent studies have focused on the restoration and translation of historical languages. |
| Approach: | They propose to use two different stimuli to priming ancient-Korean NMT . they confirm the possibility of developing a human-centric model based on cognitive science . |
| Outcome: | The proposed model can be used to translate historical Korean documents using neural machine translation. |
Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing (2024.findings-eacl)
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Chanjun Park, Jaehyung Seo, Seolhwa Lee, Junyoung Son, Hyeonseok Moon, Sugyeong Eo, Chanhee Lee, Heuiseok Lim
| Challenge: | Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts. |
| Approach: | They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing . |
| Outcome: | The proposed method can generate ASR inputs from clean text using a text-to-speech system. |
What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models? (2023.acl-long)
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| Challenge: | In the context of natural language inference, we examine how language models reason with respective readings from two perspectives: syntactic-semantic and commonsense-world knowledge. |
| Approach: | They propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResLI to encompass various explicit and implicit realizations of "respectively". |
| Outcome: | The proposed datasets include explicit and implicit readings of "respectively" the proposed dataset shows that fine-tuned models struggle with understanding readings without explicit supervision. |
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
| Approach: | They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus. |
| Outcome: | The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. |
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue (2022.lrec-1)
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| Challenge: | FreeTalky is a deep learning-based foreign language learning platform for people who experience anxiety dealing with foreign languages. |
| Approach: | They propose a deep learning-based foreign language learning platform called FreeTalky . it employs a humanoid robot NAO and various deep learning models . |
| Outcome: | The proposed system provides personalized learning based on persona dialogue and grammar error correction, and also helps alleviate xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation. |
PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities (2022.aacl-demo)
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| Challenge: | Existing software packages are expensive and difficult to use, and only provide simple functions. |
| Approach: | They propose an AI-based AAC system called PicTalky that can improve communication skills for children with language disabilities. |
| Outcome: | The proposed system improves communication skills and language comprehension abilities for children with language disabilities. |