Thanks a lot for the pointer @lewtun!
I have taken a look and tried to make some progress, but it seems I’m a bit stuck on both approaches.
1. Using mqa multilingual dataset approach
My main problem here is that this dataset has a totally different format than SQuAD, so I don’t know what type of model to fine-tune.
I’ve downloaded the relevant subsets and investigating them. The CQA part looks like forum questions with multiple answers and looks to have lower quality. The FAQ contains single questions and answer, where the quality looks good, however there is no context for these (as in the case of SQuAD). So I cannot use this to fine-tune a ModelForQuestionAnswering, right?
I gues the difference in the dataset has to do with the fact, that you mentioned, that the mqa data set is of a retrieval based/community question answering type (in contrast with SQuAD which is extractive question answering type). The haystack example you’ve linked talks about creating an embedding of the questions, and then calculating a similarity with the input/incoming user questions. Is this the path to go forward?
2. Translating SQuAD dataset approach
I don’t know what to do with the answer_start property
I’ve checked the resourses linked for this approach as well, and while translating the context/question/answer automatically with some model seems feasable to me, there is on part of the Dataset which seems problematic.
Every answer has an answer_start property, which if my assumption is correct, marks the start of the tokenized answer in the tokenized context. Now, in case the text is translated, word order will be switched and the answer_start properies will be all wrong/offset. Would this prevent the correct training of a model? How could this be solved?