Enrique Noriega-Atala — accepted AI-conference papers

2024

Active Learning Design Choices for NER with Transformers

Robert Vacareanu, Enrique Noriega-Atala, Gus Hahn-Powell, Marco A. Valenzuela-Escarcega, Mihai Surdeanu

COLING 2024main

We explore multiple important choices that have not been analyzed in conjunction regarding active learning for token classification using transformer networks. These choices are: (i) how to select what to annotate, (ii) decide whether to annotate entire sentences or smaller sentence fragments, (iii)…

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2024

When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context

Enrique Noriega-Atala, Robert Vacareanu, Salena Torres Ashton, Adarsh Pyarelal, Clayton T Morrison, Mihai Surdeanu

EMNLP 2024finding

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our app…

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2022

A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction

Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega +1

NAACL 2022system demonstrations

We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples,which are collected with a…

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