  Providing plausible responses to why questions is a challenging but critical
goal for language based human-machine interaction. Explanations are challenging
in that they require many different forms of abstract knowledge and reasoning.
Previous work has either relied on human-curated structured knowledge bases or
detailed domain representation to generate satisfactory explanations. They are
also often limited to ranking pre-existing explanation choices. In our work, we
contribute to the under-explored area of generating natural language
explanations for general phenomena. We automatically collect large datasets of
explanation-phenomenon pairs which allow us to train sequence-to-sequence
models to generate natural language explanations. We compare different training
strategies and evaluate their performance using both automatic scores and human
ratings. We demonstrate that our strategy is sufficient to generate highly
plausible explanations for general open-domain phenomena compared to other
models trained on different datasets.
