Pranav Krishna


2021

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DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan | Dragomir Radev | Rui Zhang | Amrit Rau | Abhinand Sivaprasad | Chiachun Hsieh | Xiangru Tang | Aadit Vyas | Neha Verma | Pranav Krishna | Yangxiaokang Liu | Nadia Irwanto | Jessica Pan | Faiaz Rahman | Ahmad Zaidi | Mutethia Mutuma | Yasin Tarabar | Ankit Gupta | Tao Yu | Yi Chern Tan | Xi Victoria Lin | Caiming Xiong | Richard Socher | Nazneen Fatema Rajani
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

2018

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Linking WordNet to 3D Shapes
Angel X Chang | Rishi Mago | Pranav Krishna | Manolis Savva | Christiane Fellbaum
Proceedings of the 9th Global Wordnet Conference

We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes. The goal is to establish a dataset that helps us to understand how people categorize everyday common objects via their parts, attributes, and context. This paper describes the annotation and data collection effort so far as well as ideas for future work.