CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions

Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret. May 2022. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2602–2627, Dublin, Ireland. Association for Computational Linguistics. (PDF, openreview, arXiv:2012.04293, poster).

Abstract: Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.

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Mukayese: Turkish NLP Strikes Back

Ali Safaya, Emirhan Kurtuluş, Arda Göktoğan, Deniz Yuret. May 2022. In Findings of the Association for Computational Linguistics: ACL 2022, pages 846–863, Dublin, Ireland. Association for Computational Linguistics. (PDF, openreview, arXiv:2203.01215, poster).

Abstract: Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the state-of-the-art in NLP applications. As a solution, we present Mukayese, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking. All datasets and baselines are available under: https://github.com/alisafaya/mukayese.

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