Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition

Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which we are now presenting to the public. With the KBMC dataset, we noticed an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.

NR-Surface: NextG-ready $μ$W-reconfigurable mmWave Metasurface

Metasurface has recently emerged as an economic solution to expand mmWave coverage. However, their pervasive deployment remains a challenge, mainly due to the difficulty in reaching the tight 260ns NR synchronization requirement and real-time wireless reconfiguration while maintaining multi-year battery life. This paper presents NR-Surface, the first real-time reconfigurable metasurface fully compliant with the NR standard, operating at 242.7 $\mu$W for a 2.1-year lifetime on an AA battery. NR-Surface incorporates (i) a new extremely low-power (14KHz sampling) reconfiguration interface, NarrowBand Packet Unit (NBPU), for synchronization and real-time reconfiguration, and (ii) a highly responsive and low-leakage metasurface designed for low-duty cycled operation, by carefully leveraging the structure and the periodicity of the NR beam management procedure in the NR standard. NR-Surface is prototyped and evaluated end-to-end with NR BS built on srsRAN to demonstrate diverse usage scenarios including multiple NR-Surface per BS, multiple UE per NR-Surface, and 3D beamforming. Around-the-corner UE evaluations showcase NR-Surface efficacy under different user mobility patterns (20.3dB gain) and dynamic blockage (22.2dB gain).

CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean

This paper explores the challenges posed by aspect-based sentiment classification (ABSC) within pretrained language models (PLMs), with a particular focus on contextualization and hallucination issues. In order to tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a benchmark dataset that incorporates aspects and dual-tagged polarities to distinguish between aspect-specific and aspect-agnostic sentiment classification. The dataset consists of sentences annotated with specific aspects, aspect polarity, aspect-agnostic polarity, and the intensity of aspects. To address the issue of dual-tagged aspect polarities, we propose a novel approach employing a Siamese Network. Our experimental findings highlight the inherent difficulties in accurately predicting dual-polarities and underscore the significance of contextualized sentiment analysis models. The CARBD-Ko dataset serves as a valuable resource for future research endeavors in aspect-level sentiment classification.

Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise granularity, categorizing noisy labels as clean, re-labeled, or ambiguous for robust optimization. Extensive experiments on three benchmark datasets demonstrate that BalanceMix outperforms existing state-of-the-art methods. We release the code at https://github.com/DISL-Lab/BalanceMix.

DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP

This paper presents the DaG LLM (David and Goliath Large Language Model), a language model specialized for Korean and fine-tuned through Instruction Tuning across 41 tasks within 13 distinct categories.

The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1. We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system under the standard MSS formulation achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers/musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.

Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3

In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a time-efficient music source separation model that achieves state-of-the-art results on the MUSDB benchmark. We then give full details regarding our solutions for each Leaderboard, including a loss masking approach for noise-robust training. Code for reproducing model training and final submissions is available at github.com/kuielab/sdx23.

Sound Demixing Challenge 2023 -- Music Demixing Track Technical Report

In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. We focus on two methods designed for this challenge: a time-efficient source separation network that achieves state-of-the-art results on the MUSDB benchmark and a loss masking method for noise-robust source separation. Code for reproducing model training and final submissions is available at github.com/kuielab/sdx23.

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions. However, little attention was paid to GNN's vulnerability to exposure bias: users are exposed to a limited number of items so that a system only learns a biased view of user preference to result in suboptimal recommendation quality. Although inverse propensity weighting is known to recognize and alleviate exposure bias, it usually works on the final objective with the model outputs, whereas GNN can also be biased during neighbor aggregation. In this paper, we propose a simple but effective approach, neighbor aggregation via inverse propensity (Navip) for GNNs. Specifically, given a user-item bipartite graph, we first derive propensity score of each user-item interaction in the graph. Then, inverse of the propensity score with Laplacian normalization is applied to debias neighbor aggregation from exposure bias. We validate the effectiveness of our approach through our extensive experiments on two public and Amazon Alexa datasets where the performance enhances up to 14.2%.

Meta-Learning for Online Update of Recommender Systems

Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over time, the update strategy should be flexible to quickly catch users' current interest from continuously generated new user-item interactions. Existing update strategies focus either on the importance of each user-item interaction or the learning rate for each recommender parameter, but such one-directional flexibility is insufficient to adapt to varying relationships between interactions and parameters. In this paper, we propose MeLON, a meta-learning based novel online recommender update strategy that supports two-directional flexibility. It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest. The procedure of MeLON is optimized following a meta-learning approach: it learns how a recommender learns to generate the optimal learning rates for future updates. Specifically, MeLON first enriches the meaning of each interaction based on previous interactions and identifies the role of each parameter for the interaction; and then combines these two pieces of information to generate an adaptive learning rate. Theoretical analysis and extensive evaluation on three real-world online recommender datasets validate the effectiveness of MeLON.