Papers by Aljoscha Burchardt

5 papers
Clinical Text Anonymization, its Influence on Downstream NLP Tasks and the Risk of Re-Identification (2023.eacl-srw)

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Challenge: De-identification and anonymization of clinical data is needed to solve access to clinicaldata.
Approach: They propose to use text anonymization techniques to break the anonymization of clinical data . they propose to apply a re-identification attack to the anonymized text data to break this.
Outcome: The proposed approach can break the anonymization of clinical data, the authors show .
TQ-AutoTest – An Automated Test Suite for (Machine) Translation Quality (L18-1)

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Challenge: Especially the trend towards neural MT has renewed peoples' interest in better and more analytical diagnostic methods for MT quality.
Approach: They propose a framework that supports a linguistic evaluation of machine translations using test suites.
Outcome: The proposed framework supports linguistic evaluation of (machine) translations using test suites.
Large Language Models Are Echo Chambers (2024.lrec-main)

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Challenge: Modern large language models and chatbots are subject to criticism in many aspects.
Approach: They show that large language models and chatbots are echo chambers . they annotate inputs and show that all chatbot agree .
Outcome: The proposed models show that they tend to agree with the opinions of their users.
An Annotated Corpus of Textual Explanations for Clinical Decision Support (2022.lrec-1)

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Challenge: In recent years, machine learning for clinical decision support has gained more and more attention.
Approach: They propose to use XAI to provide an explanation of a model's decision making process by constructing a corpus of sentences that are annotated with different semantic layers.
Outcome: The proposed models outperform physicians on very specific, narrow tasks or can help physicians to work more efficiently.
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)

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Challenge: Using fine-grained evaluation techniques, translation outputs have become better and more fluent.
Approach: They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail .
Outcome: The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions.

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