Abstract: Semantic role labeling (SRL) aims to extract the arguments for each predicate
in an input sentence. Traditional SRL can fail to analyze dialogues because it
only works on every single sentence, while ellipsis and anaphora frequently
occur in dialogues. To address this problem, we propose the conversational SRL
task, where an argument can be the dialogue participants, a phrase in the
dialogue history or the current sentence. As the existing SRL datasets are in
the sentence level, we manually annotate semantic roles for 3,000 chit-chat
dialogues (27,198 sentences) to boost the research in this direction.
Experiments show that while traditional SRL systems (even with the help of
coreference resolution or rewriting) perform poorly for analyzing dialogues,
modeling dialogue histories and participants greatly helps the performance,
indicating that adapting SRL to conversations is very promising for universal
dialogue understanding. Our initial study by applying CSRL to two mainstream
conversational tasks, dialogue response generation and dialogue context
rewriting, also confirms the usefulness of CSRL.