A2A: Attention to Attention Reasoning for Movie Question Answering
碩士 === 國立清華大學 === 資訊工程學系所 === 106 === This thesis presents the Attention to Attention (A2A) reasoning mecha-nism to address the challenging task of movie question answering (MQA). By focusing on the various aspects of attention cues, we establish the tech-nique of attention propagation to uncover la...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2018
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/vwu6sd |
| Summary: | 碩士 === 國立清華大學 === 資訊工程學系所 === 106 === This thesis presents the Attention to Attention (A2A) reasoning mecha-nism to address the challenging task of movie question answering (MQA). By focusing on the various aspects of attention cues, we establish the tech-nique of attention propagation to uncover latent but useful information to solving the underlying QA task. In addition, the proposed A2A reasoning seamlessly leads to effective fusion of different representation modalities about the data, and also can be conveniently constructed with popular neural network architectures. To tackle the out-of-vocabulary issue caused by the diverse language usages in nowadays movies, we adopt the GloVe mapping as a teacher model and establish a new and flexible word embed-ding based on character n-grams learning. Our method is evaluated on the MovieQA benchmark dataset and achieves the state-of-the-art accuracy for the ‘Video+Subtitles’ entry.
|
|---|