Aadit Vyas


pdf bib
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.


pdf bib
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Nazneen Fatema Rajani | Rui Zhang | Yi Chern Tan | Stephan Zheng | Jeremy Weiss | Aadit Vyas | Abhijit Gupta | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit.