人工智能 | IJCNN 2019 Special Section 征稿

Special Section

Transferable neural models for language understanding

概况

Language understanding, dealing with machine reading comprehension in various forms such as question answering, machine translation and language dialog, has been an aspiration of the artificial intelligence community, but has limited success until recently. Due to the success of deep neural networks, there is a resurgence of interest in research on deep neural networks applied to language understanding. The most recent research in language understanding aims to build deep neural network models that can be used for various language understanding tasks, such as paraphrasing, question answering, machine translation, spoken dialog, and text categorization. However, these models are (1) very data hungry – requiring large training data; (2) very task specific – hard to generalize the model for one task to other related tasks. To solve these problems, recently, transfer learning has been applied to language understanding. Transfer learning is a learning paradigm that aims to apply knowledge gained while solving one problem to a different but related problem. Transfer learning builds a neural model for one language understanding task with large training data, and then the model is retrained for another task with small training data.  

重要日期

Paper submission:     Dec 15, 2018

Paper Acceptance Notification:  January 30, 2019 

征稿范围

Topics of interest for this special session include but are not limited to: 

• Deep learning 

• Multi-task learning 

• Transfer learning 

• Active learning 

• Self-taught learning 

• Domain adaption 

• Question answering 

• Paraphrase 

• Natural language inference  

• Sequence to sequence learning 

• Natural language generation 

• Machine translation 

• Summarization 

• Information extraction 

注意事项

When submitting your paper via https://ieee-cis.org/conferences/ijcnn2019/upload.php,  please select “S33: Transferable neural models for language understanding”

联系人

Dr. Zhiwei Lin (z.lin@ulster.ac.uk)

Prof. Hui Wang (h.wang@ulster.ac.uk)