基于门控化上下文感知网络的词语释义生成方法

Gated Context-Aware Network for Definition Generation

传统的词典编纂工作主要采用人工编纂的方式,效率较低且耗费大量的资源。为减少人工编纂的时间和经济成本,该文提出一种基于门控化上下文感知网络的词语释义生成方法,利用门控循环神经网络(GRU)对词语释义生成过程进行建模,自动为目标词生成词语释义。该模型基于编码器—解码器架构。编码器首先利用双向GRU对目标词的上下文进行编码,并采用不同的匹配策略进行目标词与上下文的交互,结合注意力机制分别从粗粒度和细粒度两个层次将上下文信息融合到目标词的向量表示中,最终获得目标词在特定语境中的编码向量。解码器则同时基于目标词的语境与语义信息为目标词生成上下文相关的词语释义。此外,通过向模型提供目标词字符级特征信息,进一步提高了生成释义的质量。在英文牛津词典数据集上进行的实验表明,该文提出的方法能够生成易于阅读和理解的词语释义,在释义建模的困惑度和生成释义的BLEU值上分别超出此前模型4.45和2.19,性能有显著提升。

The traditional lexicography was mainly subject to manual compilation,which is inefficient and costs a lot of resources.This paper proposes a gated context-aware network for definition generation.It utilizes GRU to model the definitions of words and generates the textual definition for the target word automatically.The model is based on the encoder-decoder architecture.Firstly,the context of the target word is encoded by bidirectional GRU.Then,different matching strategies are used to interact the target word with context and the context information is incorporated into the target word embedding from two aspects of coarse-grained and fine-grained by the attention mechanism to obtain the meaning of the target word in a specific context.The decoding process based on the contextual and semantic information to generate context-dependent definition of the target word.In addition,the quality of generated definitions is further improved by providing the character level information of target words.The experimental results show that the proposed model improves the perplexity of definition modeling and the BLEU score of definition generation on the English Oxford dictionary dataset by 4.45 and 2.19 respectively,and can generate readable and understandable definitions.