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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
Published on: November 30, 2018
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Measuring the importance of context when modeling language comprehension.
Justin Garten1, Brendan Kennedy1, Kenji Sagae2
1Department of Computer Science, University of Southern California, 3620 S. McClintock Ave, Los Angeles, CA, 90089-1061, USA.
Behavior Research Methods
|February 21, 2019
Summary
Understanding language requires context. This study introduces a framework using Long Short-term Memory (LSTM) networks to quantify how background information influences speaker intention detection in text.
Area of Science:
- Computational Linguistics
- Natural Language Processing
- Artificial Intelligence
Background:
- Language communication relies heavily on contextual information, including speaker background and previous discourse.
- Quantifying the precise impact of context on inferring speaker intent remains a significant challenge in linguistic research.
Purpose of the Study:
- To introduce and demonstrate a methodological framework for empirically evaluating the importance of contextual information in inferring speaker intentions from text.
- To address the challenges of quantifying language and the vast combinations of contextual factors.
Main Methods:
- Application of Long Short-term Memory (LSTM) networks, a standard for sequential data representation.
- Conducting experiments to predict persuasive intentions in political debates using text and speaker background data.
Main Results:
- The proposed framework effectively models the manner and magnitude of context's relevance.
- Demonstrated the suitability of the framework for evaluating contextual influence across various domains.
Conclusions:
- The developed framework provides a robust method for quantitatively assessing the role of context in language understanding.
- This approach facilitates empirical investigation into the interaction between language, context, and inferred speaker intent.
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