Papers with RL-MIL systematically
Reinforced Multiple Instance Selection for Speaker Attribute Prediction (2024.naacl-long)
Copied to clipboard
Alireza Salkhordeh Ziabari, Ali Omrani, Parsa Hejabi, Preni Golazizian, Brendan Kennedy, Payam Piray, Morteza Dehghani
| Challenge: | Current methods for predicting speaker attributes take a speaker’s utterances as input and provide a prediction per speaker attribute. |
| Approach: | They propose a Multiple Instance Learning approach that uses Reinforcement Learning to predict speaker attributes using a set of utterances from social media posts and political ideologies from transcribed speeches. |
| Outcome: | The proposed approach outperforms existing methods on a range of related tasks including predicting speakers’ psychographics and demographics from social media posts and political ideologies from transcribed speeches. |