Tianyue Ou
2025
Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
Jiarui Liu
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Iman Ouzzani
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Wenkai Li
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Lechen Zhang
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Tianyue Ou
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Houda Bouamor
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Zhijing Jin
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Mona T. Diab
Findings of the Association for Computational Linguistics: ACL 2025
The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. This work introduces GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms were translated into Arabic, Chinese, French, Japanese, and Russian using a hybrid framework that combines LLMs for extraction with human expertise for translation. The dataset’s quality was benchmarked against existing resources, demonstrating superior translation accuracy through crowdsourced evaluation. GIST was integrated into translation workflows using post-translation refinement methods that required no retraining, where LLM prompting consistently improved BLEU and COMET scores. A web demonstration on the ACL Anthology platform highlights its practical application, showcasing improved accessibility for non-English speakers. We address a critical gap in AI terminology resources and fosters global inclusivity and collaboration in AI research.
CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation
Faria Huq
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Zora Zhiruo Wang
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Frank F. Xu
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Tianyue Ou
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Shuyan Zhou
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Jeffrey P. Bigham
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Graham Neubig
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fallshort on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the agent’s capabilities effectively. We propose CowPilot, a frame- work supporting autonomous as well as human-agent co llaborative w eb navigation, and evaluation across task success and task efficiency. CowPilot reduces the number of steps humans need to perform by allowing agents to propose next steps, while users are able to pause, reject, or take alternative actions. During execution, users can interleave their actions with the agent’s by overriding suggestions or resuming agent control when needed. We conducted case studies on five common websites and found that the human-agent collaborative mode achieves the highest success rate of 95% while requiring humans to perform only 15.2% of the total steps. Even with human interventions during task execution, the agent successfully drives up to half of task success on its own. CowPilot can serve as a useful tool for data collection and agent evaluation across websites, which we believe will enable research in how users and agents can work together. Video demonstrations are available at https://oaishi.github.io/cowpilot.html
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- Jeffrey P. Bigham 1
- Houda Bouamor 1
- Mona Diab 1
- Faria Huq 1
- Zhijing Jin 1
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