2 research outputs found
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
Static knowledge graph has been incorporated extensively into
sequence-to-sequence framework for text generation. While effectively
representing structured context, static knowledge graph failed to represent
knowledge evolution, which is required in modeling dynamic events. In this
paper, an automatic commenting task is proposed for long novels, which involves
understanding context of more than tens of thousands of words. To model the
dynamic storyline, especially the transitions of the characters and their
relations, Evolutionary Knowledge Graph(EKG) is proposed and learned within a
multi-task framework. Given a specific passage to comment, sequential modeling
is used to incorporate historical and future embedding for context
representation. Further, a graph-to-sequence model is designed to utilize the
EKG for comment generation. Extensive experimental results show that our
EKG-based method is superior to several strong baselines on both automatic and
human evaluations
A Survey of Knowledge-Enhanced Text Generation
The goal of text generation is to make machines express in human language. It
is one of the most important yet challenging tasks in natural language
processing (NLP). Since 2014, various neural encoder-decoder models pioneered
by Seq2Seq have been proposed to achieve the goal by learning to map input text
to output text. However, the input text alone often provides limited knowledge
to generate the desired output, so the performance of text generation is still
far from satisfaction in many real-world scenarios. To address this issue,
researchers have considered incorporating various forms of knowledge beyond the
input text into the generation models. This research direction is known as
knowledge-enhanced text generation. In this survey, we present a comprehensive
review of the research on knowledge enhanced text generation over the past five
years. The main content includes two parts: (i) general methods and
architectures for integrating knowledge into text generation; (ii) specific
techniques and applications according to different forms of knowledge data.
This survey can have broad audiences, researchers and practitioners, in
academia and industry.Comment: 42 pages, 12 tables, 8 figures; Under review at ACM CSUR (revised
manuscript