Papers by Eddie Eddie

6 papers
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models (2023.findings-acl)

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Challenge: Initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes.
Approach: They conduct a survey among non-cisgender individuals and interviews to establish which harms affected individuals anticipate, and how they would like to be represented.
Outcome: The results show that certain non-cisgender identities are consistently (mis)represented as less human, more stereotyped and more sexualised.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)

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Challenge: Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability.
Approach: They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations .
Outcome: The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient .
Discovering influential text using convolutional neural networks (2024.findings-acl)

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Challenge: Existing methods for estimating the effects of text on human evaluation are limited to testing a small number of pre-specified text treatments.
Approach: They propose a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks.
Outcome: The proposed method can detect and predict human reactions to texts under certain assumptions.
Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)

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Challenge: Existing knowledge graph completion models require longer training and inference times as well as increased memory usage.
Approach: They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations.
Outcome: The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets.
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software (2020.coling-main)

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Challenge: This paper describes the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages and extending their use.
Approach: They describe the first phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages.
Outcome: The proposed software will help Indigenous communities preserve and revitalize their languages and extend their use.
This prompt is measuring <mask>: evaluating bias evaluation in language models (2023.findings-acl)

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Challenge: a growing body of work uses prompts and templates to assess bias in language models . authors examine the scope of possible bias types and identify those under-researched .
Approach: They draw on a measurement modelling framework to create a bias taxonomy . they show that bias tests are often unstated or ambiguous, carry implicit assumptions .
Outcome: The proposed taxonomy shows that bias tests are often unstated or ambiguous . the analysis illuminates the scope of possible bias types the field can measure .

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