Sequence-to-Sequence Learning as Beam-Search Optimization
Table 3: Dependency parsing. UAS/LAS of seq2seq, BSO, ConBSO and baselines on PTB test set. Andor is the current state-of-the-art model for this data set (Andor et al. 2016), and we note that with a beam of size 32 they obtain 94.41/92.55. All experiments above have Ktr=6.
['[EMPTY]', 'Dependency Parsing (UAS/LAS)  [ITALIC] Kte = 1', 'Dependency Parsing (UAS/LAS)  [ITALIC] Kte = 5', 'Dependency Parsing (UAS/LAS)  [ITALIC] Kte = 10']
BSO and ConBSO both show significant improvements over seq2seq, with ConBSO improving most on UAS, and BSO improving most on LAS. We achieve a reasonable final score of 91.57 UAS, which lags behind the state-of-the-art, but is promising for a general-purpose, word-only model.