  Conversational query rewriting aims to reformulate a concise conversational
query to a fully specified, context-independent query that can be effectively
handled by existing information retrieval systems. This paper presents a
few-shot generative approach to conversational query rewriting. We develop two
methods, based on rules and self-supervised learning, to generate weak
supervision data using large amounts of ad hoc search sessions, and to
fine-tune GPT-2 to rewrite conversational queries. On the TREC Conversational
Assistance Track, our weakly supervised GPT-2 rewriter improves the
state-of-the-art ranking accuracy by 12%, only using very limited amounts of
manual query rewrites. In the zero-shot learning setting, the rewriter still
gives a comparable result to previous state-of-the-art systems. Our analyses
reveal that GPT-2 effectively picks up the task syntax and learns to capture
context dependencies, even for hard cases that involve group references and
long-turn dependencies.
