Search Results for author: Martin Volk

Found 22 papers, 4 papers with code

Improving Specificity in Review Response Generation with Data-Driven Data Filtering

no code implementations ECNLP (ACL) 2022 Tannon Kew, Martin Volk

In this work we examine the task of generating more specific responses for online reviews in the hospitality domain by identifying generic responses in the training data, filtering them and fine-tuning the generation model.

Response Generation Text Generation

Transformer-based HTR for Historical Documents

1 code implementation21 Mar 2022 Phillip Benjamin Ströbel, Simon Clematide, Martin Volk, Tobias Hodel

We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning.

Transfer Learning

Evaluation of HTR models without Ground Truth Material

1 code implementation17 Jan 2022 Phillip Benjamin Ströbel, Simon Clematide, Martin Volk, Raphael Schwitter, Tobias Hodel, David Schoch

The evaluation of Handwritten Text Recognition (HTR) models during their development is straightforward: because HTR is a supervised problem, the usual data split into training, validation, and test data sets allows the evaluation of models in terms of accuracy or error rates.

Handwritten Text Recognition Model Selection

Benchmarking Data-driven Automatic Text Simplification for German

no code implementations LREC 2020 Andreas S{\"a}uberli, Sarah Ebling, Martin Volk

Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian.

Machine Translation Text Simplification +1

Geotagging a Diachronic Corpus of Alpine Texts: Comparing Distinct Approaches to Toponym Recognition

no code implementations RANLP 2019 Tannon Kew, Anastassia Shaitarova, Isabel Meraner, Janis Goldzycher, Simon Clematide, Martin Volk

Geotagging historic and cultural texts provides valuable access to heritage data, enabling location-based searching and new geographically related discoveries.

Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation

1 code implementation EMNLP 2018 Samuel Läubli, Rico Sennrich, Martin Volk

Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese--English news translation task.

Machine Translation Translation

Crowdsourcing an OCR Gold Standard for a German and French Heritage Corpus

no code implementations LREC 2016 Simon Clematide, Lenz Furrer, Martin Volk

Crowdsourcing approaches for post-correction of OCR output (Optical Character Recognition) have been successfully applied to several historic text collections.

Optical Character Recognition

Evaluating the fully automatic multi-language translation of the Swiss avalanche bulletin

no code implementations23 May 2014 Kurt Winkler, Tobias Kuhn, Martin Volk

After being operational for two winter seasons, we assess here the quality of the produced texts based on an evaluation where participants rate real danger descriptions from both origins, the catalogue of phrases versus the manually written and translated texts.


SUMAT: Data Collection and Parallel Corpus Compilation for Machine Translation of Subtitles

no code implementations LREC 2012 Volha Petukhova, Rodrigo Agerri, Mark Fishel, Sergio Penkale, Arantza del Pozo, Mirjam Sepesy Mau{\v{c}}ec, Andy Way, Panayota Georgakopoulou, Martin Volk

Subtitling and audiovisual translation have been recognized as areas that could greatly benefit from the introduction of Statistical Machine Translation (SMT) followed by post-editing, in order to increase efficiency of subtitle production process.

14 Machine Translation +1

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