# MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering

Source: [MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering](<https://www.sota2.com/research/paper/68fa77681e209131cea73195>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:18:49.615Z

Authors: Sofian Chaybouti, Achraf Saghe, Aymen Shabou

Published: 2020-12-17T17:22:30.000Z

## Abstract

This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a BM25-based Retriever to reduce the search space, a RoBERTa-based Scorer, and an Extractor to rank retrieved paragraphs and extract relevant text spans, respectively. Eventually, we further improve the computational efficiency of our system to deal with the scalability challenge: thanks to multi-task learning, we parallelize the close tasks solved by the Scorer and the Extractor. Our system is on par with state-of-the-art performances on the squad-open benchmark while being simpler conceptually.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.09766>)

[PDF](<https://arxiv.org/pdf/2012.09766.pdf>)