Bhavana Dalvi is a Research Scientist at AI2. Her research interests are in the area of Information Extraction, Machine Learning, and Knowledge Base population. She received her PhD from the Carnegie Mellon University in 2015 and M.Tech from the Indian Institute of Technology, Bombay in 2007. Prior to her PhD, she worked at Google Bangalore for two years. Besides research, she loves hiking, meditation, and music.
[Dataset] [Dataset reader and utility code]
WIQA is the first large-scale dataset of “What if…” questions over procedural text. WIQA contains three parts: a collection of paragraphs each describing a process, e.g., beach erosion; a set of crowdsourced influence graphs for each paragraph, describing how one change affects another; and a large (40k) collection of “What if…?” multiple-choice questions derived from the graphs. For example, given a paragraph about beach erosion, would stormy weather result in more or less erosion (or have no effect)? The task is to answer the questions, given their associated paragraph.
[Dataset] [Leaderboard] [Our models]
ProPara aims to promote the research in natural language understanding in the context of procedural text. This requires identifying the actions described in the paragraph and tracking state changes happening to the entities involved. We treat the comprehension task as that of predicting, tracking, and answering questions about how entities change during the procedure. The dataset contains 488 paragraphs and 3,300 sentences. Each paragraph is richly annotated with the existence and locations of all the main entities (the “participants”) at every time step (sentence) throughout the procedure (~81,000 annotations).