Joint Decoding of Tree Transduction Models for Sentence Compression
Jin-Ge Yao, Xiaojun Wan, Jianguo Xiao · 2014
In this paper, we provide a new method for decoding tree transduction based sentence compression models augmented with lan-guage model scores, by jointly decoding two components. In our proposed so-lution, rich local discriminative features can be easily integrated without increasing computational complexity. Utilizing an unobvious fact that the resulted two com-ponents can be independently decoded, we conduct efficient joint decoding based on dual decomposition. Experimental results show that our method outperforms tradi-tional beam search decoding and achieves the state-of-the-art performance. 1