117 - Interpreting NLP Model Predictions, with Sameer Singh

We interviewed Sameer Singh for this episode, and discussed an
overview of recent work in interpreting NLP model predictions,
particularly instance-level interpretations. We started out by
talking about why it is important to interpret model outputs and
why it is a hard problem. We then dove into the details of three
kinds of interpretation techniques: attribution based methods,
interpretation using influence functions, and generating
explanations. Towards the end, we spent some time discussing how
explanations of model behavior can be evaluated, and some
limitations and potential concerns in evaluation methods. Sameer
Singh is an Assistant Professor of Computer Science at the
University of California, Irvine. Some of the techniques discussed
in this episode have been implemented in the AllenNLP Interpret
framework (details and demo here: https://allennlp.org/interpret).