  Natural language processing (NLP) models trained on people-generated data can
be unreliable because, without any constraints, they can learn from spurious
correlations that are not relevant to the task. We hypothesize that enriching
models with speaker information in a controlled, educated way can guide them to
pick up on relevant inductive biases. For the speaker-driven task of predicting
code-switching points in English--Spanish bilingual dialogues, we show that
adding sociolinguistically-grounded speaker features as prepended prompts
significantly improves accuracy. We find that by adding influential phrases to
the input, speaker-informed models learn useful and explainable linguistic
information. To our knowledge, we are the first to incorporate speaker
characteristics in a neural model for code-switching, and more generally, take
a step towards developing transparent, personalized models that use speaker
information in a controlled way.
