Sheffield at E 2 E : structured prediction approaches to end-to-end language generation

We describe the two systems, and their variations, that were submitted by the University of Sheffield to the E2E NLG challenge. Our systems consist of different approaches to structured prediction for end-to-end language generation. Our first submitted system employs imitation learning for structured prediction to explore the large search space without explicitly enumerating it. Our second submitted system uses encoder-decoder architectures to generate sequences of words. Our submitted runs for each system achieved BLEU scores of 0.60 and 0.54 respectively. On human evaluation our imitation learning model were placed in the 2nd best quality and 3rd best naturalness clusters according to Trueskill scores, while our encoder-decoder model was the best performing system on naturalness but on quality it was placed in the 5th best cluster.