Papers by Esther Goldbraich
Generating OpenAPI Specifications from Online API Documentation with Large Language Models (2025.acl-industry)
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Koren Lazar, Matan Vetzler, Kiran Kate, Jason Tsay, David Boaz, Himanshu Gupta, Avraham Shinnar, Rohith D Vallam, David Amid, Esther Goldbraich, Jim Laredo, Ateret Anaby Tavor
| Challenge: | API specifications are often presented as unstructured HTML pages, requiring external users to manually convert it into a structured format. |
| Approach: | They propose a framework that transforms long API documentation pages into consistent, machine-readable API specifications. |
| Outcome: | The proposed framework generalizes well across hundreds of APIs and produces valid OpenAPI specifications that encapsulate most of the information from the original documentation. |
Balancing via Generation for Multi-Class Text Classification Improvement (2020.findings-emnlp)
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| Challenge: | balancing is a known technique for improving classification performance . balancy is based on a balancing policy and a text generation mechanism . |
| Approach: | They propose a balancing-via-generation framework that augments a dataset for more balanced distribution by using a text generation mechanism. |
| Outcome: | The proposed framework can augment a dataset for more balanced distribution while under-sampling others. |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |