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We describe our entry for the Systematic Review Information Extraction trackof the 2018 Text Analysis Conference. Our solution is an end-to-end, deeplearning, sequence tagging model based on the BI-LSTM-CRF architecture.However, we use interleaved, alternating LSTM layers with highway connectionsinstead of the more traditional approach, where last hidden states of bothdirections are concatenated to create an input to the next layer. We also makeextensive use of pre-trained word embeddings, namely GloVe and ELMo. Thanks toa number of regularization techniques, we were able to achieve relatively largecapacity of the model (31.3M+ of trainable parameters) for the size of trainingset (100 documents, less than 200K tokens). The system's official score was60.9% (micro-F1) and it ranked first for the Task 1. Additionally, afterrectifying an obvious mistake in the submission format, the system scored67.35%.

Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents