In recent semantic role labeling (SRL) competitions such as the shared tasks of CoNLL 2005 and CoNLL 2008, supervised SRL systems have been trained on newswire text, and then tested on both an in-domain test set (Wall Street Journal text) and an out-of-domain test set (fiction).

We aim to build an open-domain supervised SRL system; that is, one whose performance on out-of-domain tests approaches the same level of performance as that of state-of-the-art systems on in-domain tests.

Experiments on out-of-domain test sets show that our learned representations can dramatically improve out—of-domain performance, and narrow the gap between in-domain and out-of-domain performance by half.

The aim of the evaluation is to measure the reachable accuracy of the simple model proposed and to compare its impact over in-domain and out-of-domain semantic role labeling tasks.

The in-domain test has been run over the FrameNet annotated corpus, derived from the British National Corpus (BNC).

training FN—BNC 134,697 271,560 test in-domain FN—BNC 14,952 30,173 t d .