# Incremental Reading for Question Answering

Source: [Incremental Reading for Question Answering](<https://www.sota2.com/research/paper/68fa654e1e209131cea097f9>)

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

Data updated: 2025-10-25T21:23:52.793Z

Authors: Samira Abnar, Tania Bedrax-weiss, Tom Kwiatkowski, William W. Cohen

Published: 2019-01-15T17:03:32.000Z

## Abstract

Any system which performs goal-directed continual learning must not only learn incrementally but process and absorb information incrementally. Such a system also has to understand when its goals have been achieved. In this paper, we consider these issues in the context of question answering. Current state-of-the-art question answering models reason over an entire passage, not incrementally. As we will show, naive approaches to incremental reading, such as restriction to unidirectional language models in the model, perform poorly. We present extensions to the DocQA \[2\] model to allow incremental reading without loss of accuracy. The model also jointly learns to provide the best answer given the text that is seen so far and predict whether this best-so-far answer is sufficient.

## 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/1901.04936>)

[PDF](<https://arxiv.org/pdf/1901.04936.pdf>)