Niko Schenk


2020

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Translation Inference by Concept Propagation
Christian Chiarcos | Niko Schenk | Christian Fäth
Proceedings of the 2020 Globalex Workshop on Linked Lexicography

This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin.

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Towards the First Machine Translation System for Sumerian Transliterations
Ravneet Punia | Niko Schenk | Christian Chiarcos | Émilie Pagé-Perron
Proceedings of the 28th International Conference on Computational Linguistics

The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.

2018

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Knowing the Author by the Company His Words Keep
Armin Hoenen | Niko Schenk
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The ACoLi CoNLL Libraries: Beyond Tab-Separated Values
Christian Chiarcos | Niko Schenk
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Towards a Linked Open Data Edition of Sumerian Corpora
Christian Chiarcos | Émilie Pagé-Perron | Ilya Khait | Niko Schenk | Lucas Reckling
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
Samuel Rönnqvist | Niko Schenk | Christian Chiarcos
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model’s ability to selectively focus on the relevant parts of an input sequence.

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Resource-Lean Modeling of Coherence in Commonsense Stories
Niko Schenk | Christian Chiarcos
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We present a resource-lean neural recognizer for modeling coherence in commonsense stories. Our lightweight system is inspired by successful attempts to modeling discourse relations and stands out due to its simplicity and easy optimization compared to prior approaches to narrative script learning. We evaluate our approach in the Story Cloze Test demonstrating an absolute improvement in accuracy of 4.7% over state-of-the-art implementations.

2016

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Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling
Niko Schenk | Christian Chiarcos
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling
Niko Schenk | Christian Chiarcos | Kathrin Donandt | Samuel Rönnqvist | Evgeny Stepanov | Giuseppe Riccardi
Proceedings of the CoNLL-16 shared task

2015

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Towards the Unsupervised Acquisition of Implicit Semantic Roles
Niko Schenk | Christian Chiarcos | Maria Sukhareva
Proceedings of the International Conference Recent Advances in Natural Language Processing

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A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition
Christian Chiarcos | Niko Schenk
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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Memory-Based Acquisition of Argument Structures and its Application to Implicit Role Detection
Christian Chiarcos | Niko Schenk
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Towards Semantic Language Classification: Inducing and Clustering Semantic Association Networks from Europarl
Steffen Eger | Niko Schenk | Alexander Mehler
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics