Here, we investigate the impact of transductive learning on state-of-the-art neural models in syntactic and semantic tasks, namely syntactic chunking and semantic role labeling (SRL).
inspired by recent findings that language model (LM)-based word representations yield large performance improvement (Devlin et al., 2019), we fine-tune Embeddings from Language Models (ELMo)  on an unlabeled test set and use them in each task-specific model.