The vast majority of textual content is unstructured, making automated classification an important task for many applications. The goal of text classification ...
10.12.2021 · %0 Conference Proceedings %T Understanding Attention for Text Classification %A Sun, Xiaobing %A Lu, Wei %S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics %D 2020 %8 jul %I Association for Computational Linguistics %C Online %F sun-lu-2020-understanding %X Attention has been proven successful in many natural …
27.11.2020 · Understanding Attention for Text Classification. This is the supplementary materials and Pytorch code for the paper Understanding Attention for Text Classification. Attention has been proven successful in many natural language processing (NLP) tasks. Recently, many researchers started to investigate the interpretability of attention on NLP tasks.
Understanding Attention for Text Classification Xiaobing Sun and Wei Lu StatNLP Research Group Singapore University of Technology and Design xiaobing sun@mymail.sutd.edu.sg, luwei@sutd.edu.sg Abstract ... Understanding Attention for Text Classification ...
How NLP models translate from input to output sequence. The input sequence and the encoder model are shown in yellow, while the output sequence and the decoder ...
As such, we should pay attention to how our text is converted to the numbers that our attention model can understand. For our raw text, we need to do some ...
Attention has been proven successful in many natural language processing (NLP) tasks. Recently, many researchers started to investigate the interpretability of ...
Download Citation | On Jan 1, 2020, Xiaobing Sun and others published Understanding Attention for Text Classification | Find, read and cite all the research you need on ResearchGate