Papers by Kisu Yang
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
Copied to clipboard
| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
Reliable Evaluation Protocol for Low-Precision Retrieval (2026.acl-short)
Copied to clipboard
| Challenge: | Recent studies have shown that low-precision methods can improve performance, but they introduce high variability in the results based on tie resolution. |
| Approach: | They propose a retrieval evaluation protocol designed to reduce tie variation . high-precision scoring and tie-aware retrieval metrics are proposed to reduce this variability . |
| Outcome: | The proposed retrieval evaluation protocol reduces tie-induced instability and recovers expected scores and ranges on 12 retrieval datasets. |
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations (2023.emnlp-main)
Copied to clipboard
Yoonna Jang, Suhyune Son, Jeongwoo Lee, Junyoung Son, Yuna Hur, Jungwoo Lim, Hyeonseok Moon, Kisu Yang, Heuiseok Lim
| Challenge: | Despite advances in language generation, models suffer from hallucinations that are either untrue or unfaithful to a given source. |
| Approach: | They propose a method to refine hallucinated utterances based on source knowledge . REM implicitly uses key entities in the knowledge to refine the utterant . |
| Outcome: | The proposed method reduces entity hallucination in the generated utterance and improves the quality of the model. |
PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities (2022.aacl-demo)
Copied to clipboard
| 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. |