Papers by Christian Matt

2 papers
Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies (2024.acl-long)

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Challenge: a decade ago a single metric, BLEU, governed progress in machine translation research.
Approach: They investigate the "dynamic range" of a number of modern machine translation metrics to provide a collective understanding of differences in scores . they use a large dataset to discover deltas at which metrics achieve system-level differences that are meaningful to humans .
Outcome: The proposed method is more stable than statistical p-values in regards to testset size.
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry (2025.emnlp-industry)

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Challenge: Recent trends in NLP utilize knowledge graphs to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked.
Approach: They propose to use graph-aware neighborhood contrastive learning methodology SciNCL to enhance pretrained language models by incorporating additional knowledge from graph structures.
Outcome: The proposed graph-aware neighborhood contrastive learning methodology outperforms a state-of-the-art mE5-large text encoder on the process industry text embedding benchmark while having 3 times fewer parameters.

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