no code implementations • EMNLP (FEVER) 2021 • Laura Mascarell, Tatyana Ruzsics, Christian Schneebeli, Philippe Schlattner, Luca Campanella, Severin Klingler, Cristina Kadar
Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
1 code implementation • 16 Jul 2024 • Anum Afzal, Ribin Chalumattu, Florian Matthes, Laura Mascarell
Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different domains.
1 code implementation • 23 May 2024 • Laura Mascarell, Yan L'Homme, Majed El Helou
Understanding the nature of high-quality summaries is crucial to further improve the performance of multi-document summarization.
1 code implementation • 6 Mar 2024 • Laura Mascarell, Ribin Chalumattu, Annette Rios
The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks.
no code implementations • WS 2017 • Laura Mascarell
Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation.
no code implementations • EACL 2017 • Xiao Pu, Laura Mascarell, Andrei Popescu-Belis
We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers{'} output, and also use these methods in combination.