상세정보
학술지
Sentence-Chain Based Seq2seq Model for Corpus Expansion
- 발행일
- 201708
- 출처
- ETRI Journal, v.39 no.4, pp.455-466
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 초록
- This study focuses on a method for sequential data augmentation in order to alleviate data sparseness problems. Specifically, we present corpus expansion techniques for enhancing the coverage of a language model. Recent recurrent neural network studies show that a seq2seq model can be applied for addressing language generation issues; it has the ability to generate new sentences from given input sentences. We present a method of corpus expansion using a sentence-chain based seq2seq model. For training the seq2seq model, sentence chains are used as triples. The first two sentences in a triple are used for the encoder of the seq2seq model, while the last sentence becomes a target sequence for the decoder. Using only internal resources, evaluation results show an improvement of approximately 7.6% relative perplexity over a baseline language model of Korean text. Additionally, from a comparison with a previous study, the sentence chain approach reduces the size of the training data by 38.4% while generating 1.4-times the number of ngrams with superior performance for English text.
- KSP 제안 키워드
- Data Augmentation, Data sparseness, Language generation, Language model, Recurrent Neural Network(RNN), Sequential data, chain based, superior performance, training data
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