added konvens paper

content/post/konvens2023/index.md

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+---
+# Documentation: https://wowchemy.com/docs/managing-content/
+
+title: "1 paper to be presented at KONVENS 2023"
+subtitle: ""
+summary: ""
+authors: []
+tags: []
+categories: []
+date: 2023-08-17T09:24:01+02:00
+lastmod: 2023-08-17
+featured: false
+draft: false
+
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+---
+
+One paper from DFKI-NLP researchers has been accepted for publication at KONVENS 2023, the 19th German Conference on Natural Language Processing. The conference will take place in Ingolstadt, Germany, from Sep 18th to Sep 22nd, 2023. The paper presents an approach using machine translation to translate English data to German to train a transformer-based factuality detection model for clinical data, where supervised data is usually very scarce due to its sensitive nature and privacy concerns.
+
+{{< cite page="/publication/konvens2023-binsumait-etal-factuality" view="4" >}}

content/publication/konvens2023-binsumait-etal-factuality/cite.bib

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+@inproceedings{binsumait-etal-2023-factuality,
+    title = "Factuality Detection using Machine Translation - a Use Case for German Clinical Text",
+    author = "Bin Sumait, Mohammed and Gabryszak, Aleksandra and Hennig, Leonhard and Roller, Roland",
+    booktitle = "Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)",
+    month = sep,
+    year = "2023",
+    address = "Ingolstadt, Germany",
+    publisher = "KONVENS 2023 Organizers",
+    abstract = "Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of examples is necessary to handle such phenomena in a supervised machine learning setting. However, as clinical text might contain sensitive information, data cannot be easily shared. In the context of factuality detection, this work presents a simple solution using machine translation to translate English data to German to train a transformer-based factuality detection model.",
+}

content/publication/konvens2023-binsumait-etal-factuality/index.md

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+---
+# Documentation: https://wowchemy.com/docs/managing-content/
+
+title: "Factuality Detection using Machine Translation - a Use Case for German Clinical Text"
+authors: ["Mohammed Bin Sumait", "Aleksandra Gabryszak", "Leonhard Hennig", "Roland Roller"]
+date: 2023-08-17T10:33:03+02:00
+#doi: "10.48550/arXiv.2308"
+
+# Schedule page publish date (NOT publication's date).
+publishDate: 2023-08-17T10:33:03+02:00
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: "Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)"
+publication_short: "KONVENS 2023"
+
+abstract: "Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of examples is necessary to handle such phenomena in a supervised machine learning setting. However, as clinical text might contain sensitive information, data cannot be easily shared. In the context of factuality detection, this work presents a simple solution using machine translation to translate English data to German to train a transformer-based factuality detection model."
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+categories: []
+featured: false
+
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+---