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pro vyhledávání: '"Zhang, Xinliang Frederick"'
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language models (LLMs) have demonstrated impressive performance in many reasoning ta
Externí odkaz:
http://arxiv.org/abs/2410.05558
Structural extraction of events within discourse is critical since it avails a deeper understanding of communication patterns and behavior trends. Event argument extraction (EAE), at the core of event-centric understanding, is the task of identifying
Externí odkaz:
http://arxiv.org/abs/2401.13218
News media often strive to minimize explicit moral language in news articles, yet most articles are dense with moral values as expressed through the reported events themselves. However, values that are reflected in the intricate dynamics among partic
Externí odkaz:
http://arxiv.org/abs/2311.09733
Autor:
Liu, Yujian, Zhang, Xinliang Frederick, Zou, Kaijian, Huang, Ruihong, Beauchamp, Nick, Wang, Lu
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topi
Externí odkaz:
http://arxiv.org/abs/2310.18827
News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via lingui
Externí odkaz:
http://arxiv.org/abs/2310.18768
Annotator disagreement is ubiquitous in natural language processing (NLP) tasks. There are multiple reasons for such disagreements, including the subjectivity of the task, difficult cases, unclear guidelines, and so on. Rather than simply aggregating
Externí odkaz:
http://arxiv.org/abs/2305.14663
Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a t
Externí odkaz:
http://arxiv.org/abs/2211.02269
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for
Externí odkaz:
http://arxiv.org/abs/2211.01467
Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-d
Externí odkaz:
http://arxiv.org/abs/2205.00619
Autor:
Zhang, Xinliang Frederick
Natural Language Understanding (NLU) is a branch of Natural Language Processing (NLP) that uses intelligent computer software to understand texts that encode human knowledge. Recent years have witnessed notable progress across various NLU tasks with
Externí odkaz:
http://arxiv.org/abs/2112.02992