Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lin, Zizheng"'
Theory-of-Mind (ToM) ability possessed by Large Language Models (LLMs) has been shown to be limited. Most existing methods for improving ToM in LLMs adopt zero-shot prompting, and they face challenges including poor performance in complex ToM reasoni
Externí odkaz:
http://arxiv.org/abs/2409.13490
Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints wit
Externí odkaz:
http://arxiv.org/abs/2302.04459
Various data mining tasks have been proposed to study Community Question Answering (CQA) platforms like Stack Overflow. The relatedness between some of these tasks provides useful learning signals to each other via Multi-Task Learning (MTL). However,
Externí odkaz:
http://arxiv.org/abs/2110.02059
Autor:
Zeng, Ziqian, Zhou, Wenxuan, Liu, Xin, Lin, Zizheng, Song, Yangqin, Kuo, Michael David, Chiu, Wan Hang Keith
In this paper, we propose a variational approach to unsupervised sentiment analysis. Instead of using ground truth provided by domain experts, we use target-opinion word pairs as a supervision signal. For example, in a document snippet "the room is b
Externí odkaz:
http://arxiv.org/abs/2008.09394
Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art
Externí odkaz:
http://arxiv.org/abs/1908.11049
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates
Externí odkaz:
http://arxiv.org/abs/1806.03256