- Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg, Jianfeng Gao. 2021. Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text. Abstract: Due to its potential for a universal interface 001 over both data and text, data-to-text genera- 002 tion is becoming increasingly popular. How- 003 ever, few prior work has focused on its ap- 004 plication to downstream tasks, e.g. using the 005 converted data for grounding or reasoning. 006 In this work, we bridge this gap and use 007 the data-to-text method as a means for en- 008 coding structured knowledge for knowledge- 009 intensive applications, i.e. open-domain ques- 010 tion answering (ODQA). Speciﬁcally, we pro- 011 pose a verbalizer-retriever-reader framework 012 for ODQA over data and text where verbal- 013 ized tables from Wikipedia and graphs from 014 Wikidata are used as augmented knowledge 015 sources. We show that our U niﬁed D ata and 016 T ext QA , UDT-QA , can effectively beneﬁt 017 from the expanded knowledge index, leading 018 to large gains over text-only baselines. No- 019 tably, our approach sets the single-model state- 020 of-the-art on Natural Questions. Furthermore, 021 our analyses indicate that verbalized knowl- 022 edge is preferred for answer reasoning for both 023 adapted and hot-swap settings. 024
