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Mihaela Bornea

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3 papers

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AAAI Conference 2023 System Paper

GAAMA 2.0: An Integrated System That Answers Boolean and Extractive Questions

Scott McCarley
Mihaela Bornea
Sara Rosenthal
Anthony Ferritto
Md Arafat Sultan
Avirup Sil
Radu Florian

Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a front-end demo to a multilingual machine reading comprehension system that handles boolean and extractive questions. It provides a yes/no answer and highlights the supporting evidence for boolean questions. It provides an answer for extractive questions and highlights the answer in the passage. Our system, GAAMA 2.0, achieved first place on the TyDi QA leaderboard at the time of submission. We contrast two different implementations of our approach: including multiple transformer models for easy deployment, and a shared transformer model utilizing adapters to reduce GPU memory footprint for a resource-constrained environment.

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AAAI Conference 2021 System Paper

Exploring the Efficacy of Generic Drugs in Treating Cancer

Ioana Baldini
Mariana Bernagozzi
Sulbha Aggarwal
Mihaela Bornea
Saksham Chawla
Joppe Geluykens
Dmitriy A. Katz-Rogozhnikov
Pratik Mukherjee

Thousands of scientific publications discuss evidence on the efficacy of non-cancer generic drugs being tested for cancer. However, trying to manually identify and extract such evidence is intractable at scale. We introduce a natural language processing pipeline to automate the identification of relevant studies and facilitate the extraction of therapeutic associations between generic drugs and cancers from PubMed abstracts. We annotate datasets of drug-cancer evidence and use them to train models to identify and characterize such evidence at scale. To make this evidence readily consumable, we incorporate the results of the models in a web application that allows users to browse documents and their extracted evidence. Users can provide feedback on the quality of the evidence extracted by our models. This feedback is used to improve our datasets and the corresponding models in a continuous integration system. We describe the natural language processing pipeline in our application and the steps required to deploy services based on the machine learning models.

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AAAI Conference 2021 Conference Paper

Multilingual Transfer Learning for QA using Translation as Data Augmentation

Mihaela Bornea
Lin Pan
Sara Rosenthal
Radu Florian
Avirup Sil

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages. In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space. Our first strategy augments the original English training data with machine translation-generated data. This results in a corpus of multilingual silver-labeled QA pairs that is 14 times larger than the original training set. In addition, we propose two novel strategies, language adversarial training and language arbitration framework, which significantly improve the (zero-resource) cross-lingual transfer performance and result in LM embeddings that are less language-variant. Empirically, we show that the proposed models outperform the previous zero-shot baseline on the recently introduced multilingual MLQA and TYDI QA datasets.

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