GooAQ: Open Question Answering with Diverse Answer Types

TL;DR

GooAQ is a large-scale dataset from Google search responses that captures diverse answer types, aiming to improve question-answering models' ability to generate varied and natural answers.

Contribution

The paper introduces GooAQ, a novel dataset with diverse answer types collected from Google, and benchmarks T5 models, highlighting the importance of pre-training for complex answer generation.

Findings

LM performance improves with annotated data for short answers

Pre-training is crucial for generating coherent long responses

GooAQ enables research on diverse answer type question answering

Abstract

While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmarkT5 models on GooAQ and observe that: (a) in line with recent work, LM's strong performance on GooAQ's short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as 'how' and 'why' questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.