Intended Behaviour for Impossible (out-of-span) SQuAD Features
## ❓ Questions & Help

Hello! We have a quick question regarding the featurization for BERT/XLNet Question Answering.

We noticed a confusing contradiction in your current `utils_squad` implementation: regardless of how the `version_2_with_negative` flag is set, you do not discard “impossible” features (chunks of a context). Instead of discarding them, you train on them but with the span start and end indices pointing to the [CLS] token. However, this comment in your code indicates that you *do* intend to discard such features (at least for SQuAD 1.1 we would assume): https://github.com/huggingface/pytorch-transformers/blob/a7b4cfe9194bf93c7044a42c9f1281260ce6279e/examples/utils_squad.py#L332-L333.

We noticed that this behavior is the same with the Google TensorFlow BERT repository, though we see no reference in their paper to training SQuAD 1.1 with impossible contexts. Should we assume for SQuAD 1.1 the `max_sequence_length` was just always longer that all SQuAD contexts, and thus no "impossible" features were produced?

Ultimately, we are wondering if this behavior is intentional or not for purely extractive QA (like SQuAD 1.1, as opposed to 2.0)? Are you aware of anyone using “impossible" inputs to train a model for extractive QA without an abstention objective?

Thank you for your time and insights!
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