In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

Shengqi Zhu, Jeffrey Rzeszotarski. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Event temporal relation extraction (ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label.This formulation ignores the meaning of relations and wipes out their intrinsic dependency.After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events.For example, relation Includes could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs.Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3% on both datasets.By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios.When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations.The code is released at https://github.com/AndrewZhe/ A-Unified-Framework-for-ETRE.

DocRED is a widely used dataset for documentlevel relation extraction.In the large-scale annotation, a recommend-revise scheme is adopted to reduce the workload.Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remove relational facts based on the recommendations.However, when comparing Do-cRED with a subset relabeled from scratch, we find that this scheme results in a considerable amount of false negative samples and an obvious bias towards popular entities and relations.Furthermore, we observe that the models trained on DocRED have low recall on our relabeled dataset and inherit the same bias in the training data.Through the analysis of annotators' behaviors, we figure out the underlying reason for the problems above: the scheme actually discourages annotators from supplementing adequate instances in the revision phase.We appeal to future research to take into consideration the issues with the recommend-revise scheme when designing new models and annotation schemes.The relabeled dataset is released at https://github. com/AndrewZhe/Revisit-DocRED, to serve as a more reliable test set of document RE models.

Quzhe Huang, Shengqi Zhu, Yansong Feng, Dongyan Zhao. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.