Papers by Vicente Ordonez
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation (2022.lrec-1)
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Samhita Honnavalli, Aesha Parekh, Lily Ou, Sophie Groenwold, Sharon Levy, Vicente Ordonez, William Yang Wang
| Challenge: | Existing studies have not investigated how gender biases in natural language processing (NLP) are compounded with other societal biase. |
| Approach: | They propose a framework for probing compound bias by examining seniority in pre-trained neural generation models. |
| Outcome: | The proposed framework amplifies bias by considering women as junior and men as senior more often than ground truth in both domains. |
Gender Bias in Contextualized Word Embeddings (N19-1)
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| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
| Approach: | They conduct several intrinsic analyses to quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. |
| Outcome: | The proposed method mitigates gender bias on WinoBias probing corpus and demonstrates that it can be implemented in other systems. |
Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Chat-crowd: A Dialog-based Platform for Visual Layout Composition (N19-4)
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| Challenge: | We present Chat-crowd, an interactive environment for visual layout composition via conversational interactions . system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection . |
| Approach: | They introduce an interactive environment for visual layout composition via conversational interactions that supports multiple agents with two conversational roles. |
| Outcome: | The proposed system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and has quality controls on the performance of both types of agents. |
Using Visual Feature Space as a Pivot Across Languages (2020.findings-emnlp)
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| Challenge: | We show that models trained to generate textual captions in more than one language can leverage their jointly trained feature space during inference to pivot across languages. |
| Approach: | They show that models trained to generate captions in more than one language can leverage their jointly trained feature space during inference to pivot across languages. |
| Outcome: | The proposed approach improves quality of captions in German and English by leveraging captions from a second language. |
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (N18-2)
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| Challenge: | Existing methods for co-reference resolution focus on gender bias. |
| Approach: | They propose a new benchmark for co-reference resolution focused on gender bias, WinoBias. |
| Outcome: | The proposed system removes the bias without significantly affecting performance on existing datasets. |
Visual News: Benchmark and Challenges in News Image Captioning (2021.emnlp-main)
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| Challenge: | Visual News Captioner is an entity-aware model for news image captioning . Unlike standard image captions, news images depict situations where people, locations, and events are of paramount importance. |
| Approach: | They propose a visual news captioner model that integrates visual and textual features to generate captions with richer information such as events and entities. |
| Outcome: | The proposed model can generate captions with richer information such as events and entities. |
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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| Challenge: | Existing methods to debias word embeddings from human-generated corpora inherit strong gender bias . prior work has suggested removing gender component from pre-trained word embeds or compressing gender information into a few dimensions of the embeddable space . |
| Approach: | They propose a technique that purifies word embeddings against inferred gender subspaces . they propose to preserve distributional semantics of pre-trained word embeds while reducing gender bias . |
| Outcome: | The proposed technique preserves distributional semantics of pre-trained word embeddings while reducing gender bias to a larger degree than prior approaches. |
PropTest: Automatic Property Testing for Improved Visual Programming (2024.findings-emnlp)
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| Challenge: | Visual Programming is an alternative to end-to-end black-box visual reasoning models. |
| Approach: | They propose a visual programming strategy that leverages Large Language Models to generate the logic of a program in the form of its source code. |
| Outcome: | The proposed method improves ViperGPT on visual question answering and referring expression comprehension with an LLM. |