Papers by Soyoung Yoon
Exploring the Practicality of Generative Retrieval on Dynamic Corpora (2024.emnlp-main)
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| Challenge: | a lack of comprehensive comparison between GR and Dual Encoders in IR systems is limiting . GR is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, and efficient in terms of inference FLOPs (x2), indexing time (x6) and storage footprint (x4) |
| Approach: | They propose to use autoregressive language models to perform information retrieval (IR) their results highlight the potential of GR for future use in practical IR systems . |
| Outcome: | The proposed model is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, efficient inference FLOPs (x2), indexing time (x6), and storage footprint (x4) compared to the most common model, Dual Encoder (DE). |
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text. |
| Approach: | They propose a pipeline that expands existing evaluation sets by adding alternative valid terms for aspect and opinion. |
| Outcome: | The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets. |
RoToR: Towards More Reliable Responses for Order-Invariant Inputs (2025.acl-long)
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| Challenge: | Existing solutions to positional bias in listwise inputs are limited on practical problems . e.g., lost-in-the-middle problem is a common problem for listwise models . |
| Approach: | They propose a zero-shot order-invariant LM for order- invariant inputs with minimal modifications of positional IDs and Selective Routing for listwise tasks. |
| Outcome: | The proposed framework can handle order-invariant and sensitive inputs in listwise tasks. |
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation (2023.acl-long)
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| Challenge: | Despite the growing number of Korean learners, little research has been conducted on Korean grammatical error correction (GEC) despite the difficulties of the Korean language, there is no evaluation benchmark for Korean GEC. |
| Approach: | They propose to use Korean grammar error correction datasets to train a machine learning model that can automatically annotate Korean errors from parallel corpora. |
| Outcome: | The proposed model outperforms the currently used statistical Korean GEC system on a wider range of error types. |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
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| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |
SSMix: Saliency-Based Span Mixup for Text Classification (2021.findings-acl)
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| Challenge: | SSMix synthesizes a sentence while preserving the locality of two original texts by span-based mixing and keeping more tokens related to the prediction relying on saliency information. |
| Approach: | They propose a new method where the operation is performed on input text rather than on hidden vectors like previous approaches. |
| Outcome: | The proposed method outperforms hidden-level mixup methods on a wide range of text classification benchmarks including textual entailment, sentiment classification, and questiontype classification. |
An Integrated Search System for Korea Weather Data (2023.emnlp-industry)
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| Challenge: | a weather search system is used to retrieve weather data from a massive weather database . a lack of navigation and time-consuming navigation hinders accurate weather forecasting . |
| Approach: | They propose a weather search system that allows users to retrieve weather data from a massive weather database with simple queries. |
| Outcome: | The proposed system achieves an average MRR and Recall of 0.82 on 4 million data points . it is based on a weather database at the Korea Meteorological Administration . |