  The automatic annotation of direct speech (AADS) in written text has been
often used in computational narrative understanding. Methods based on either
rules or deep neural networks have been explored, in particular for English or
German languages. Yet, for French, our target language, not many works exist.
Our goal is to create a unified framework to design and evaluate AADS models in
French. For this, we consolidated the largest-to-date French narrative dataset
annotated with DS per word; we adapted various baselines for sequence labelling
or from AADS in other languages; and we designed and conducted an extensive
evaluation focused on generalisation. Results show that the task still requires
substantial efforts and emphasise characteristics of each baseline. Although
this framework could be improved, it is a step further to encourage more
research on the topic.
