Ridho Reinanda


2023

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TempTabQA: Temporal Question Answering for Semi-Structured Tables
Vivek Gupta | Pranshu Kandoi | Mahek Vora | Shuo Zhang | Yujie He | Ridho Reinanda | Vivek Srikumar
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TEMPTABQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.

2014

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Prior-informed Distant Supervision for Temporal Evidence Classification
Ridho Reinanda | Maarten de Rijke
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers