Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Simpson, Edwin"'
Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually based on n-gram overlap and embedding similarity, but are reported fai
Externí odkaz:
http://arxiv.org/abs/2409.15090
Detecting out-of-distribution (OOD) data is crucial in machine learning applications to mitigate the risk of model overconfidence, thereby enhancing the reliability and safety of deployed systems. The majority of existing OOD detection methods predom
Externí odkaz:
http://arxiv.org/abs/2408.11237
Autor:
Ghazawi, Rayed, Simpson, Edwin
Automated Essay Scoring (AES) holds significant promise in the field of education, helping educators to mark larger volumes of essays and provide timely feedback. However, Arabic AES research has been limited by the lack of publicly available essay d
Externí odkaz:
http://arxiv.org/abs/2407.11212
In our study, we first constructed a dataset from the tweets of the top 100 medical influencers with the highest Influencer Score during the COVID-19 pandemic. This dataset was then used to construct a socio-semantic network, mapping both their ident
Externí odkaz:
http://arxiv.org/abs/2407.05198
Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiolog
Externí odkaz:
http://arxiv.org/abs/2405.10842
Autor:
Ye, Yuxuan, Simpson, Edwin
This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform
Externí odkaz:
http://arxiv.org/abs/2211.07596
Autor:
Treviso, Marcos, Lee, Ji-Ung, Ji, Tianchu, van Aken, Betty, Cao, Qingqing, Ciosici, Manuel R., Hassid, Michael, Heafield, Kenneth, Hooker, Sara, Raffel, Colin, Martins, Pedro H., Martins, André F. T., Forde, Jessica Zosa, Milder, Peter, Simpson, Edwin, Slonim, Noam, Dodge, Jesse, Strubell, Emma, Balasubramanian, Niranjan, Derczynski, Leon, Gurevych, Iryna, Schwartz, Roy
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data,
Externí odkaz:
http://arxiv.org/abs/2209.00099
Peer review is the primary means of quality control in academia; as an outcome of a peer review process, program and area chairs make acceptance decisions for each paper based on the review reports and scores they received. Quality of scientific work
Externí odkaz:
http://arxiv.org/abs/2109.01190
Autor:
Siekiera, Julia, Köppel, Marius, Simpson, Edwin, Stowe, Kevin, Gurevych, Iryna, Kramer, Stefan
The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require substantial amounts of labeled data that are difficult and expensive t
Externí odkaz:
http://arxiv.org/abs/2010.12613