Online Segment to Segment Neural Transduction
Table 1: ROUGE F1 scores on the sentence summarisation test set. Seq2seq refers to the vanilla encoder-decoder and attention denotes the attention-based model. SSNT denotes our model with alignment transition probability modelled as geometric distribution. SSNT+ refers to our model with transition probability modelled using neural networks. The prefixes uni- and bi- denote using unidirectional and bidirectional encoder LSTMs, respectively.
['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Our models achieve significantly better results than the vanilla encoder-decoder and outperform the attention-based model. The fact that SSNT+ performs better is in line with our expectations, as the neural network-parameterised alignment model is more expressive than that modelled by geometric distribution.