Open-domain extractive question answering works well on textual data by first retrieving candidate texts and then extracting the answer from those candidates. However, some questions cannot be answered by text alone but require information stored in
tables. In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space. To this end, we create a new multi-modal dataset based on text and table datasets from related work and compare the retrieval performance of different encoding schemata. We find that dense vector embeddings of transformer models outperform sparse embeddings on four out of six evaluation datasets. Comparing different dense embedding models, tri-encoders with one encoder for each question, text and table increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables. We release the newly created multi-modal dataset to the community so that it can be used for training and evaluation.
The main objective of this paper is to verify the difference between water - steam properties obtained from Vukalovich tables (1940) and these obtained by IAPWS-IF97 model. Vukalovich tables are used as the main reference at Damascus University – Fac
ulty of Mechanical and Electrical Engineering – to acquire water and steam parameters used for vapor power cycles (Rankine Cycle). The last revised release on IAPWS-IF97 (International Association for the Properties of Water and Steam of the Industrial Formulation 1997) was in 2012. IAPWS members are United States of American, Canada, Argentina, Brazil, Britain, Ireland, Czech Republic, Denmark, France, Germany, Greece, Italy,
Switzerland (associate member) Japan, and Russia.
