ReQA: An Evaluation for End-to-End Answer Retrieval Models
TL
TL;DR
Retrieval Question Answering (ReQA), a benchmark for evaluating large-scale sentence- and paragraph-level answer retrieval models, is introduced and baselines are established using both neural encoding models as well as classical information retrieval techniques.
Abstract
Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.
References36 items
1
Natural Questions: A Benchmark for Question Answering Research
2
Multilingual Universal Sentence Encoder for Semantic Retrieval
3
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
4
Training Neural Response Selection for Task-Oriented Dialogue Systems
5