Call for Papers | IJCNN 2019 Special Section 徵稿通道開啟

Special Section: Transferable neural models for language understanding

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.

IJCNN 2019 會議論文截止日期為 2018 年 12 月 15 日，錄用通知日期為 2019 年 1 月 30 日。

徵稿主題包括但不限於：自然語言理解，推理和生成，深度學習，遷移學習，主動學習，自我學習，領域適應學習，序列對序列學習，機器翻譯，複述，問答系統，資訊抽取等。 

線上投稿：

https://ieee-cis.org/conferences/ijcnn2019/upload.php 

請選擇 Main research topic 為 S33 — Transferable neural models for language understanding。 

若想進一步瞭解，請聯絡 Dr. Zhiwei Lin（z.lin@ulster.ac.uk）。

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