Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Question Answering is a crucial natural language processing task. This field of research has
attracted a sudden amount of interest lately due mainly to the integration of the deep …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Interpretable multi-hop reading comprehension (RC) over multiple documents is a
challenging problem because it demands reasoning over multiple information sources and …

Abstract The task of Question Answering (QA) has attracted significant research interest for a
long time. Its relevance to language understanding and knowledge retrieval tasks, along …

The lack of word boundaries information has been seen as one of the main obstacles to
develop a high performance Chinese named entity recognition (NER) system. Fortunately …

Open domain question answering (ODQA) is a longstanding task aimed at answering factual
questions from a large knowledge corpus without any explicit evidence in natural language …

The problem of Question Answering (QA) has attracted significant research interest for long.
Its relevance to language understanding and knowledge retrieval tasks, along with the …

We consider the task of answering complex multi-hop questions using a corpus as a virtual
knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses …