| Title | Few-Shot Transfer Learning for Text Classification With Lightweight Word Embedding Based Models |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Pan, C., Huang, J., Gong, J., Yuan, X. |
| Journal | IEEE Access |
| Volume | 7 |
| Pagination | 53296–53304 |
| ISSN | 2169-3536 |
| Keywords | compositionality, Computational modeling, Computing Theory and Compositionality, Data models, data-hungry deep models, Deep Learning, deep learning architectures, feature extraction, Few-shot learning, few-shot transfer learning tasks, Human Behavior, human factors, lightweight word embedding-based models, modified hierarchical pooling strategy, parameter-free pooling operations, parameter-free property, parameters training, pattern classification, plug-and-play way, pooling strategy, pubcrawl, semantic compositionality, semantic networks, semantic text, simple word embedding-based models, supervised data, supervised learning, Task Analysis, text analysis, text categorization, text classification, Training, transfer learning, unseen text sequences, word embedding based models, word processing |
| Abstract | Many deep learning architectures have been employed to model the semantic compositionality for text sequences, requiring a huge amount of supervised data for parameters training, making it unfeasible in situations where numerous annotated samples are not available or even do not exist. Different from data-hungry deep models, lightweight word embedding-based models could represent text sequences in a plug-and-play way due to their parameter-free property. In this paper, a modified hierarchical pooling strategy over pre-trained word embeddings is proposed for text classification in a few-shot transfer learning way. The model leverages and transfers knowledge obtained from some source domains to recognize and classify the unseen text sequences with just a handful of support examples in the target problem domain. The extensive experiments on five datasets including both English and Chinese text demonstrate that the simple word embedding-based models (SWEMs) with parameter-free pooling operations are able to abstract and represent the semantic text. The proposed modified hierarchical pooling method exhibits significant classification performance in the few-shot transfer learning tasks compared with other alternative methods. |
| DOI | 10.1109/ACCESS.2019.2911850 |
| Citation Key | pan_few-shot_2019 |
Few-Shot Transfer Learning for Text Classification With Lightweight Word Embedding Based Models