# Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing

arXiv: https://arxiv.org/abs/2310.18229 · HTML: https://mlsanity.com/paper/2310.18229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Can neural machine translation do simultaneous translation?](https://mlsanity.com/paper/1606.02012/) (2016)
- [Re-translation versus Streaming for Simultaneous Translation](https://mlsanity.com/paper/2004.03643/) (2020)
- [Multilingual Language Models Predict Human Reading Behavior](https://mlsanity.com/paper/2104.05433/) (2021)
- [Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://mlsanity.com/paper/1902.10068/) (2019)
- [Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye](https://mlsanity.com/paper/1906.11889/) (2019)
- [Improving sentence compression by learning to predict gaze](https://mlsanity.com/paper/1604.03357/) (2016)
- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU](https://mlsanity.com/paper/2109.07364/) (2021)
- [Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU](https://mlsanity.com/paper/2010.05330/) (2020)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [Incremental Natural Language Processing: Challenges, Strategies, and Evaluation](https://mlsanity.com/paper/1805.12518/) (2018)
- [A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements](https://mlsanity.com/paper/1809.08031/) (2018)
- [TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model](https://mlsanity.com/paper/2305.10845/) (2023)
- [Efficient Encoders for Streaming Sequence Tagging](https://mlsanity.com/paper/2301.09244/) (2023)
- [The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling](https://mlsanity.com/paper/2307.15508/) (2023)

## Abstract (arXiv)

In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input. Some tokens trigger revisions, causing edits to the output hypothesis, but little is known about why models revise when they revise. A policy that detects the time steps where revisions should happen can improve efficiency. Still, retrieving a suitable signal to train a revision policy is an open problem, since it is not naturally available in datasets. In this work, we investigate the appropriateness of regressions and skips in human reading eye-tracking data as signals to inform revision policies in incremental sequence labelling. Using generalised mixed-effects models, we find that the probability of regressions and skips by humans can potentially serve as useful predictors for revisions in BiLSTMs and Transformer models, with consistent results for various languages.