Semi-Supervised Sequence Modeling with Cross-View Training

EMNLP 2018 Kevin ClarkMinh-Thang LuongChristopher D. ManningQuoc V. Le

Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
COMPARE
CCG Supertagging CCGBank Clark et al. Accuracy 96.1 # 1
Named Entity Recognition CoNLL 2003 (English) CVT + Multi-Task F1 92.61 # 9
Machine Translation IWSLT2015 English-Vietnamese CVT BLEU 29.6 # 3
Named Entity Recognition Ontonotes v5 (English) CVT + Multi-Task F1 88.81 # 4
Dependency Parsing Penn Treebank CVT + Multi-Task POS --- # 4
Dependency Parsing Penn Treebank CVT + Multi-Task UAS 96.61 # 1
Dependency Parsing Penn Treebank CVT + Multi-Task LAS 95.02 # 1