Joint Morphological Generation and Syntactic Linearization

Authors

  • Linfeng Song Chinese Academy of Science
  • Yue Zhang Singapore University of Technology and Design
  • Kai Song Northeastern University
  • Qun Liu Dublin City University and Chinese Academy of Science

DOI:

https://doi.org/10.1609/aaai.v28i1.8927

Abstract

There has been growing interest in stochastic methods to natural language generation (NLG). While most NLG pipelines separate morphological generation and syntactic linearization, the two tasks are closely related. In this paper, we study joint morphological generation and linearization, making use of word order and inflections information for both tasks and reducing error propagation. Experiments show that the joint method significantly outperforms a strong pipelined baseline (by 1.1 BLEU points). It also achieves the best reported result on the Generation Challenge 2011 shared task.

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Published

2014-06-21

How to Cite

Song, L., Zhang, Y., Song, K., & Liu, Q. (2014). Joint Morphological Generation and Syntactic Linearization. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8927

Issue

Section

Main Track: NLP and Knowledge Representation