TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
This dataset provides a challenging testbed for reading comprehension research, addressing the need for more realistic and difficult benchmarks in natural language processing.
The authors introduced TriviaQA, a large-scale reading comprehension dataset with over 650K question-answer-evidence triples, featuring complex questions and requiring cross-sentence reasoning, where baseline algorithms achieved only 23% and 40% accuracy compared to 80% human performance.
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/