Search Results for author: Marc de Kamps

Found 4 papers, 0 papers with code

BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation

no code implementations LREC 2022 Yuqian Dai, Marc de Kamps, Serge Sharoff

Pre-trained transformer-based models, such as BERT, have shown excellent performance in most natural language processing benchmark tests, but we still lack a good understanding of the linguistic knowledge of BERT in Neural Machine Translation (NMT).

Machine Translation NMT +1

Syntactic Knowledge via Graph Attention with BERT in Machine Translation

no code implementations22 May 2023 Yuqian Dai, Serge Sharoff, Marc de Kamps

Although the Transformer model can effectively acquire context features via a self-attention mechanism, deeper syntactic knowledge is still not effectively modeled.

Graph Attention Machine Translation +2

GATology for Linguistics: What Syntactic Dependencies It Knows

no code implementations22 May 2023 Yuqian Dai, Serge Sharoff, Marc de Kamps

Moreover, GAT is more competitive in training speed and syntactic dependency prediction than MT-B, which may reveal a better incorporation of modeling explicit syntactic knowledge and the possibility of combining GAT and BERT in the MT tasks.

Graph Attention Machine Translation

Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform

no code implementations14 Jan 2022 Benedikt Feldotto, Cristian Soare, Alois Knoll, Piyanee Sriya, Sarah Astill, Marc de Kamps, Samit Chakrabarty

We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model.

Electromyography (EMG)

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