# TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

## TL;DR

TriviaQA is a large, challenging reading comprehension dataset with complex questions and evidence, designed for advancing question-answering research beyond existing datasets.

## Contribution

Introduces TriviaQA, a large-scale dataset with high-quality distant supervision, complex questions, and evidence, providing a new benchmark for reading comprehension models.

## Key findings

- TriviaQA contains over 650K question-answer-evidence triples.
- Current models significantly underperform compared to humans on TriviaQA.
- TriviaQA's questions require more complex reasoning than previous datasets.

## Abstract

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study. Data and code available at -- http://nlp.cs.washington.edu/triviaqa/