Search Results for author: Daniel Varab

Found 8 papers, 4 papers with code

MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

1 code implementation EMNLP 2021 Daniel Varab, Natalie Schluter

We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting.

With Good MT There is No Need For End-to-End: A Case for Translate-then-Summarize Cross-lingual Summarization

no code implementations31 Aug 2024 Daniel Varab, Christian Hardmeier

Recent work has suggested that end-to-end system designs for cross-lingual summarization are competitive solutions that perform on par or even better than traditional pipelined designs.

Experimental Standards for Deep Learning in Natural Language Processing Research

1 code implementation13 Apr 2022 Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.

DaNewsroom: A Large-scale Danish Summarisation Dataset

no code implementations LREC 2020 Daniel Varab, Natalie Schluter

To support the comparison of future automatic summarisation systems for Danish, we include system performance on this dataset of strong well-established unsupervised baseline systems, together with an oracle extractive summariser, which is the first account of automatic summarisation system performance for Danish.

Abstractive Text Summarization

When data permutations are pathological: the case of neural natural language inference

1 code implementation EMNLP 2018 Natalie Schluter, Daniel Varab

Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline.

Natural Language Inference

UniParse: A universal graph-based parsing toolkit

1 code implementation WS (NoDaLiDa) 2019 Daniel Varab, Natalie Schluter

This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package.

Dependency Parsing

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