000			01062nam a22001937a 4500
003			OSt
005			20220107122851.0
008			190620b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aDebajyoti Chatterjee (91617007) _915903
245			_aMaking neural machine reading comprehension faster
300			_aMSC MI 2017-2019
500			_aThis study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage. The challenge is to develop a computationally faster model which will have improved inference time. State of the art in many natural language understanding tasks, BERT model, has been used and knowledge distillation method has been applied to train two smaller models. The developed models are compared with other models which have been developed with the same intention.
502			_bMSC MI _c2017-2019 _dINT _eDr Asharaf S
650			_aMACHINE READING COMPREHENSION _915904
650			_aBERT MODEL _915905
650			_aNATURAL LANGUAGE PROCESSING _915906
942			_2ddc _cPR
999			_c6508 _d6508