activity
20162026
most citedExploring the Use of Text Classification in the Legal Domain

105 citations · 245 across the 61 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.CL2019

Analysing Coreference in Transformer Outputs

Ekaterina Lapshinova-Koltunski, Cristina España-Bonet, Josef van Genabith

We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra- and cross-sentential ana…

cs.CL2019★ 1 cited

Lipschitz Constrained Parameter Initialization for Deep Transformers

Hongfei Xu, Qiuhui Liu, Josef van Genabith +2

The Transformer translation model employs residual connection and layer normalization to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. Pre…

cs.CL2019

The Transference Architecture for Automatic Post-Editing

Santanu Pal, Hongfei Xu, Nico Herbig +3

In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input. This has led to mult…

cs.CL2019★ 3 cited

Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation

Mihaela Vela, Santanu Pal, Marcos Zampieri +2

This paper describes strategies to improve an existing web-based computer-aided translation (CAT) tool entitled CATaLog Online. CATaLog Online provides a post-editing environment w…

cs.CL2019

UDS--DFKI Submission to the WMT2019 Similar Language Translation Shared Task

Santanu Pal, Marcos Zampieri, Josef van Genabith

In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. The first edition of this shared task featured data from three p…

cs.CL2019

UdS Submission for the WMT 19 Automatic Post-Editing Task

Hongfei Xu, Qiuhui Liu, Josef van Genabith

In this paper, we describe our submission to the English-German APE shared task at WMT 2019. We utilize and adapt an NMT architecture originally developed for exploiting context in…