Synchronous Bidirectional Inference for Neural Sequence Generation
Table 7: Abstractive summarization quality on DUC 2004 and English Gigaword for different methods.
['Model', 'DUC-2004 R1', 'DUC-2004 R2', 'DUC-2004 R-L', 'English Gigaword R1', 'English Gigaword R2', 'English Gigaword R-L']
Abstractive sentence summarization is another well-known testbed for sequence to sequence learning. We then apply our synchronous bidirectional inference model into this task. In contrast, our method attempts to improve the decoder (inference module) by enabling synchronous bidirectional decoding. After applying our synchronous bidirectional inference model, BI-RNMT and BIFT respectively achieve significantly better results on two test sets over RNMT and Transformer, despite that these two models cannot outperform Selective-Env on the Gigaword test set. The reason behind may be that we just apply BI-RNMT and BIFT into abstractive summarization without any special adaptation processing. From another perspective, the two kinds of the models handle encoder and decoder respectively, and can be complementary to each other. Nevertheless, the statistics given in the table further show the effectiveness of our synchronous bidirectional inference model beyond machine translation.