Papers by Vivi Nastase

6 papers
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)

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Challenge: Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor.
Approach: They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing.
Outcome: The proposed system averages 0.87 on complex sentences on the targeted relations.
BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks (2023.eacl-main)

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Challenge: Existing benchmarks for deep learning are based on massive amounts of data, which are effective in hiding some of the shallowness of the learned models.
Approach: They propose to use a French dataset to learn the underlying rules of subject-verb agreement in sentences, inspired by visual IQ tests known as Raven’s Progressive Matrices.
Outcome: The proposed method is based on Raven’s Progressive Matrices, a visual IQ test, and a dataset built using the BLM framework.
DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion (L18-1)

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Challenge: a text fragment is discarded when it has a smaller context, causing it to acquire a new meaning or even become false.
Approach: They build a dataset to study the effect of modifiers on the larger context . they focus on single-word modifiers, the smallest unit that can be considered disposable .
Outcome: The proposed dataset aims to determine whether modifiers can be removed without undesirable consequences.
Punctuation as Native Language Interference (C18-1)

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Challenge: Numerous aspects of written language have been studied for native language identification (NLI) but its impact has not been studied.
Approach: They propose to use punctuation marks as indicators of native language . they propose to apply them to native language identification .
Outcome: The proposed methods support the hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, even when a high proficiency level in a non-native language is achieved.
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods (L18-1)

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Challenge: Optical Character Recognition (OCR) can produce a range of errors depending on the quality of the original document.
Approach: They applied a sequence-to-sequence machine translation system to correct word-single-word OCR errors in scientific texts from the ACL collection with an estimated precision and recall above 0.95 on test data.
Outcome: The proposed system corrects word-segmentation OCR errors with an estimated precision and recall above 0.95 on test data.
BLM-s/lE: A structured dataset of English spray-load verb alternations for testing generalization in LLMs (2023.findings-emnlp)

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Challenge: Current NLP models are achieving performance comparable to human capabilities on well-established benchmarks.
Approach: They propose a BLM task to identify a missing element in a linguistic pattern from a list of candidate options based on a given matrix.
Outcome: The proposed framework is based on the spray-load verb alternations in English as a case study.

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