The explosive development of social media has generated a large number of slang words in Chinese social network. The appearance of Chinese slang words has affected the accuracy of reading comprehension and word segmentation tasks. In this paper, we propose explaining Chinese slang word automatically for the first time. Unlike matching words in dictionary, we use a novel neural network called DCEAnn (a Dual Character-level Encoder using Attention-based neural network) for this specific task. One encodes slang word and its phonetics to learn the word representation, the other encodes example sentence containing slang word to enrich the semantic information of the slang word. Besides, we propose a public dataset for the first time to deal with the absence of parallel corpus for training model. Manual evaluation of experimental results shows that our model can generate reasonable explanations. Furthermore, we find that our model has a better performance on the network digital language which only contains numbers. To be specific, we get the state-of-the-art result on Chinese slang words interpretation whose BLEU score is 23.64, 3.59 higher than our baseline, and the state-of-the-art result on network digital language interpretation whose BLEU score is 54.23, 3.18 higher than our baseline.
[1]
Tat-Seng Chua,et al.
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
,
2012,
WSDM '12.
[2]
Doug Downey,et al.
Definition Modeling: Learning to Define Word Embeddings in Natural Language
,
2016,
AAAI.
[3]
Alok Ranjan Pal,et al.
Detection of Slang Words in e-Data using semi-Supervised Learning
,
2017,
ArXiv.
[4]
Joelle Pineau,et al.
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
,
2015,
AAAI.
[5]
Yoshua Bengio,et al.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
,
2014,
EMNLP.
[6]
William Yang Wang,et al.
Learning to Explain Non-Standard English Words and Phrases
,
2017,
IJCNLP.
[7]
Yoshua Bengio,et al.
Neural Machine Translation by Jointly Learning to Align and Translate
,
2014,
ICLR.
[8]
Zhiyuan Liu,et al.
Neural Sentiment Classification with User and Product Attention
,
2016,
EMNLP.
[9]
David M. W. Powers,et al.
Chinese word segments based on contextual entropy
,
2003
.
[10]
Quoc V. Le,et al.
Sequence to Sequence Learning with Neural Networks
,
2014,
NIPS.