Papers by Max Smith

2 papers
Speaker Naming in Movies (N18-1)

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Challenge: Identifying speakers and their names in movies is a primary task for many video analysis problems, such as automatic subtitle labeling.
Approach: They propose a model that leverages visual, textual, and acoustic modalities in an unified optimization framework for speaker naming in movies.
Outcome: The proposed model outperforms baseline models on the MovieQA 2017 challenge for speaker naming in movies and TV shows on visual, textual, and acoustic modalities.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding (2024.lrec-main)

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Challenge: Pre-trained language models can struggle in specialized domains such as medicine . existing generalpurpose pre-tried models can be used and refined through further pre-training on domainspecific unlabeled data.
Approach: They pre-trained German medical language models on 2.4B tokens from translated public data and 3B token of German clinical data.
Outcome: The proposed models outperform clinical models on various downstream tasks in germany . the authors show that continuous pre-training can match or exceed clinical models trained from scratch .

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