& & NA & NA & 68.90 & & 72.9 & 72.9 & 72.9 & & 73.0 & 72.9\
SST-5 & & 54.7 & 51.4 & 51.30 $\pm$ 0.77 & & 52.96 $\pm$ 2.26 & 53.58 $\pm$ 0.77 & 53.80 $\pm$ 0.73 & & 53.86 $\pm$ 4.02 & 53.38 $\pm$ 0.68\

NER & & 92.22 & 90.15 & 90.97 $\pm$ 0.43 & & 92.51 $\pm$ 0.28 & 92.28 $\pm$ 0.20 & 92.24 $\pm$ 0.10 & & 92.03 $\pm$ 0.47 & 92.24 $\pm$ 0.36\
SRL & & 84.6 & 81.4 & 80.2 & & 83.4 & 82.7 & 82.4 & & 82.2 & 82.8\

#### Downstream Tasks

We follow ELMo and use the *feature-based* approach to evaluate contextual representations on downstream benchmarks. ELMo was evaluated on six benchmarks and we conduct evaluations on five of them. SQuAD [@Rajpurkar2016Squad] is not available for implementation reasons[^10]. We briefly review the benchmarks and task-specific models. For detailed descriptions, please refer to .

**SNLI**: The textual entailment task seeks to determine whether a “hypothesis” can be entailed from a “premise”. The dataset is the SNLI dataset [@SNLIDataset] and the model is ESIM [@ESIM].

**Coref**: Coreference resolution is the task of clustering mentions in text that 