Given the task definition and input, reply with output. In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Our second approach incorporates shallow syntactic information in downstream tasks via token-level chunk label embeddings. Task training (and test) data is automatically chunked, and chunk boundary information is passed into the task model via BIOUL encoding of the labels. We add randomly initialized chunk label embeddings to task-specific input encoders, which are then fine-tuned for task-specific objectives.
Question: Which syntactic features are obtained automatically on downstream task data?
token-level chunk label embeddings chunk boundary information is passed into the task model via BIOUL encoding of the labels