| Challenge: | a particular problem for anaphora resolution systems is the pronoun it, which can be used both referentially and non-referentially. |
| Approach: | They use eye-tracking data to learn how humans perform disambiguation and use it to improve automatic classification. |
| Outcome: | The proposed system outperforms a baseline and outperformed linguistic-based approaches. |
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| Challenge: | Recent work uses gaze data at the type level or at the token level and mostly from a single eye-tracking corpus. |
| Approach: | They propose to use gaze data to capture central tendency or variability of gaze data and to integrate binary phrase chunking and part-of-speech tagging. |
| Outcome: | The proposed approaches capture the central tendency or variability of gaze data better than proposed local views which retain individual participant information. |
Entity Recognition at First Sight: Improving NER with Eye Movement Information (N19-1)
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| Challenge: | Previous studies have shown eye-tracking data can be used to improve natural language processing models. |
| Approach: | They leverage eye movement features from three corpora with recorded gaze information to augment a neural model for named entity recognition with gaze embeddings. |
| Outcome: | The proposed model outperforms baseline models on both individual datasets and in cross-domain settings. |
Towards Making a Dependency Parser See (D19-1)
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| Challenge: | Eye trackers and gaze features collected from them have been recently applied to natural language processing (NLP) tasks such as part-of-speech tagging. |
| Approach: | They propose to leverage eye-tracking data in an RNN dependency parser when no aggregated or token-level gaze features are used at inference time. |
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Native Language Prediction from Gaze: a Reproducibility Study (2023.acl-srw)
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| Challenge: | Existing studies have shown that the linguistic properties of a speaker’s native language affect the cognitive processing of other languages. |
| Approach: | They found that the correlation between eye movements and native language similarity may be more complex than the original study found. |
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Visual Referring Expression Recognition: What Do Systems Actually Learn? (N18-2)
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| Challenge: | Existing systems for referring expression recognition ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. |
| Approach: | They propose to use a system trained on the input image without the input referring expression to achieve a precision of 71.2% in top-2 predictions. |
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Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity (2022.findings-aacl)
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| Challenge: | Referential gaze is a fundamental phenomenon for psycholinguistics and human-human communication. |
| Approach: | They propose a multimodal NLP task to predict when the gaze is referential . they train a sequential attention-based LSTM model and a transformer encoder architecture to model referential gaze and transfer gaze features to unseen situated settings . |
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Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour (2020.aacl-main)
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| Challenge: | Using gaze behaviour to solve automatic essay grading tasks is costly in terms of time and money. |
| Approach: | They propose to collect gaze behaviour from 48 essays and learn gaze behaviour for the rest of the essays using a multi-task learning framework. |
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Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts) (2025.naacl-tutorial)
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| Challenge: | NAACL 2025 tutorial sessions are a cornerstone event of the conference . tutorials are designed to equip you with the latest insights, tools, and methodologies . |
| Approach: | NAACL 2025 will host a tutorial session on computational linguistics and natural language processing . the tutorials are a cornerstone event of the conference . |
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Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations (2024.lrec-main)
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| Challenge: | We compare webcam-based eye-tracking recordings with human-annotated rationales to evaluate importance scores. |
| Approach: | They compare webcam-based eye-tracking recordings with attention-based importance scores for 4 different multilingual Transformer-based language models. |
| Outcome: | The proposed method is comparable to human rationales in linguistic analysis. |