Large Margin Neural Language Model
Table 6: Rescore 10-best list for dev (nist 06) and test (nist 08) set. Digits in bold are the best and italics are the runner-ups.
['rescoring language model', 'BLEU dev06', 'BLEU test08', 'PPL dev06', 'PPL test08']
To put this into perspective, Shen et al. So our NMT model is already very competitive. To construct the training data for LMLM and rLMLM, 10 beam candidates are extracted for every sentence in the training set. In addition, we also find that normalizing the LM score by sentence length can improve the re-scoring performance substantially. Tab. LMLM and rLMLM both improve upon the baseline significantly, and outperform RNNLM and RNNLM-adapted by a notable margin. We also observe that the PPLs of LMLM and rLMLM are much larger than those of RNNLM and RNNLM-adapted, suggesting that the PPL metric may be very poorly correlated with BLEU.