  While conversational semantic role labeling (CSRL) has shown its usefulness
on Chinese conversational tasks, it is still under-explored in non-Chinese
languages due to the lack of multilingual CSRL annotations for the parser
training. To avoid expensive data collection and error-propagation of
translation-based methods, we present a simple but effective approach to
perform zero-shot cross-lingual CSRL. Our model implicitly learns
language-agnostic, conversational structure-aware and semantically rich
representations with the hierarchical encoders and elaborately designed
pre-training objectives. Experimental results show that our model outperforms
all baselines by large margins on two newly collected English CSRL test sets.
More importantly, we confirm the usefulness of CSRL to non-Chinese
conversational tasks such as the question-in-context rewriting task in English
and the multi-turn dialogue response generation tasks in English, German and
Japanese by incorporating the CSRL information into the downstream
conversation-based models. We believe this finding is significant and will
facilitate the research of non-Chinese dialogue tasks which suffer the problems
of ellipsis and anaphora.
