The use of machine learning (ML) to detect depression in online settings has emerged as an
important health and wellness use case. In particular, the use of deep learning methods for …

Chinese idioms pose a significant challenge for machine reading comprehension due to
their metaphorical meanings often diverging from their literal counterparts, leading to …

We introduce a principled approach to Outlier-Efficient Attention Layers via associative
memory models to reduce outlier emergence in large transformer-based model. Our main …

The combination of Large Language Models (LLM) and Automatic Speech Recognition
(ASR), when deployed on edge devices (called edge ASR-LLM), can serve as a powerful …

We introduce SMUTF, a unique approach for large-scale tabular data schema matching
(SM), which assumes that supervised learning does not affect performance in open-domain …

中文成语有着独特的抽象语义. 在机器阅读理解任务中, 为了解决模型不能充分理解成语含义的
问题, 本文提出一种完形填空式匹配网络. 匹配网络将成语与其词典释义以注意力交互的方式 …

In this paper, we introduce a robotic agent specifically designed to analyze external
environments and address participants' questions. The primary focus of this agent is to assist …