| Paper ID | HLT-17.3 |
| Paper Title |
LANGUAGE MODEL IS ALL YOU NEED: NATURAL LANGUAGE UNDERSTANDING AS QUESTION ANSWERING |
| Authors |
Mahdi Namazifar, Alexandros Papangelis, Gokhan Tur, Dilek Hakkani-Tur, Amazon, United States |
| Session | HLT-17: Language Understanding 5: Question Answering and Reading Comprehension |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning. In this work we study the use of a specific family of transfer learning, where the target domain is mapped to the source domain. Specifically we map Natural Language Understanding (NLU) problems to Question Answering (QA) problems and we show that in low data regimes this approach offers significant improvements compared to other approaches to NLU. Moreover we show that these gains could be increased through sequential transfer learning across NLU problems from different domains. We show that our approach could reduces the amount of required data for the same performance by up to a factor of 10. |
