Papers by Laura Nguyen
On learning and representing social meaning in NLP: a sociolinguistic perspective (2021.naacl-main)
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| Challenge: | linguistic variation allows for the expression of social meaning, information about the social background and identity of the language user. |
| Approach: | They introduce the concept of social meaning to NLP and discuss how sociolinguistics can inform work on representation learning in NLP. |
| Outcome: | The proposed model can be used to learn social meaning in NLP and identify key challenges. |
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies (2026.findings-acl)
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Myke C. Cohen, Mingqian Zheng, Neel Bhandari, Hsien-Te Kao, Xuhui Zhou, Daniel Nguyen, Laura Cassani, Maarten Sap, Svitlana Volkova
| Challenge: | In simulations, personality traits and AI attributes were comparatively influential, but with actual human subjects, AI attributes – particularly transparency – were much more impactful. |
| Approach: | They compare a purely simulated dataset and a parallel human subjects experiment to examine how human personality traits and AI design characteristics jointly shape interaction outcomes in imperfectly cooperative scenarios. |
| Outcome: | The results show that personality traits and AI attributes are comparatively influential in simulations, but with actual human subjects, they are much more impactful. |
Skim-Attention: Learning to Focus via Document Layout (2021.findings-emnlp)
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| Challenge: | Existing approaches to document understanding have high computational and memory costs. |
| Approach: | They propose a new attention mechanism that takes advantage of the structure of a document and its layout. |
| Outcome: | The proposed attention mechanism obtains lower perplexity than previous studies while being more computationally efficient. |
LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization (2023.eacl-main)
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| Challenge: | Text Summarization is a popular task and a challenge for neural models. |
| Approach: | They propose to exploit visual/layout information to capture long-range dependencies in summarization models by combining layout-aware and long-reaching models. |
| Outcome: | The proposed datasets cover French, Spanish, Portuguese, and Korean languages. |