Search Results for author: Mihaela Bornea

Found 8 papers, 0 papers with code

Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering

no code implementations15 Apr 2021 Sara Rosenthal, Mihaela Bornea, Avirup Sil

Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage.

Question Answering

Multilingual Transfer Learning for QA Using Translation as Data Augmentation

no code implementations10 Dec 2020 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.

Cross-Lingual Transfer Data Augmentation +4

Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification

no code implementations IJCNLP 2019 Oren Melamud, Mihaela Bornea, Ken Barker

In this work, we combine these two approaches to improve low-shot text classification with two novel methods: a simple bag-of-words embedding approach; and a more complex context-aware method, based on the BERT model.

General Classification Text Classification +1

Stacking With Auxiliary Features for Entity Linking in the Medical Domain

no code implementations WS 2017 Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker

In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System.

Entity Linking

Scoring Disease-Medication Associations using Advanced NLP, Machine Learning, and Multiple Content Sources

no code implementations WS 2016 D, Bharath ala, Murthy Devarakonda, Mihaela Bornea, Christopher Nielson

In predicting positive associations, the stacked combination significantly outperformed the baseline (a distant semi-supervised method on large medical text), achieving F scores of 0. 75 versus 0. 55 on the pairs seen in the patient records, and F scores of 0. 69 and 0. 35 on unique pairs.

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