  Recent progress in NLP witnessed the development of large-scale pre-trained
language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al.
2017), and in a range of end tasks, such models have achieved state-of-the-art
results, approaching human performance. This demonstrates the power of the
stacked self-attention architecture when paired with a sufficient number of
layers and a large amount of pre-training data. However, on tasks that require
complex and long-distance reasoning where surface-level cues are not enough,
there is still a large gap between the pre-trained models and human
performance. Strubell et al. (2018) recently showed that it is possible to
inject knowledge of syntactic structure into a model through supervised
self-attention. We conjecture that a similar injection of semantic knowledge,
in particular, coreference information, into an existing model would improve
performance on such complex problems. On the LAMBADA (Paperno et al. 2016)
task, we show that a model trained from scratch with coreference as auxiliary
supervision for self-attention outperforms the largest GPT-2 model, setting the
new state-of-the-art, while only containing a tiny fraction of parameters
compared to GPT-2. We also conduct a thorough analysis of different variants of
model architectures and supervision configurations, suggesting future
directions on applying similar techniques to other problems.
