  Is it possible to use natural language to intervene in a model's behavior and
alter its prediction in a desired way? We investigate the effectiveness of
natural language interventions for reading-comprehension systems, studying this
in the context of social stereotypes. Specifically, we propose a new language
understanding task, Linguistic Ethical Interventions (LEI), where the goal is
to amend a question-answering (QA) model's unethical behavior by communicating
context-specific principles of ethics and equity to it. To this end, we build
upon recent methods for quantifying a system's social stereotypes, augmenting
them with different kinds of ethical interventions and the desired model
behavior under such interventions. Our zero-shot evaluation finds that even
today's powerful neural language models are extremely poor ethical-advice
takers, that is, they respond surprisingly little to ethical interventions even
though these interventions are stated as simple sentences. Few-shot learning
improves model behavior but remains far from the desired outcome, especially
when evaluated for various types of generalization. Our new task thus poses a
novel language understanding challenge for the community.
