Andreas Fischer


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Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German
Lucy Linder | Michael Jungo | Jean Hennebert | Claudiu Cristian Musat | Andreas Fischer
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents SwissCrawl, the largest Swiss German text corpus to date. Composed of more than half a million sentences, it was generated using a customized web scraping tool that could be applied to other low-resource languages as well. The approach demonstrates how freely available web pages can be used to construct comprehensive text corpora, which are of fundamental importance for natural language processing. In an experimental evaluation, we show that using the new corpus leads to significant improvements for the task of language modeling.


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Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes
Noémien Kocher | Christian Scuito | Lorenzo Tarantino | Alexandros Lazaridis | Andreas Fischer | Claudiu Musat
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In sequence modeling tasks the token order matters, but this information can be partially lost due to the discretization of the sequence into data points. In this paper, we study the imbalance between the way certain token pairs are included in data points and others are not. We denote this a token order imbalance (TOI) and we link the partial sequence information loss to a diminished performance of the system as a whole, both in text and speech processing tasks. We then provide a mechanism to leverage the full token order information—Alleviated TOI—by iteratively overlapping the token composition of data points. For recurrent networks, we use prime numbers for the batch size to avoid redundancies when building batches from overlapped data points. The proposed method achieved state of the art performance in both text and speech related tasks.