Context (archaeology)

Engineering

Query optimization

Biology

Syntax

Search engine

Economics

Computer science

Query language

Paleontology

Natural language processing

Mechanical engineering

Web search query

Ranking (information retrieval)

Organic chemistry

Generative grammar

Task (project management)

Management

Query expansion

Artificial intelligence

Rewriting

Information retrieval

Chemistry

Programming language

Web query classification

Shot (pellet)

One shot

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.

Few-Shot Generative Conversational Query Rewriting