no code implementations • ACL 2017 • Avinesh P. V. S, Christian M. Meyer
In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback.
no code implementations • IJCNLP 2017 • Tobias Falke, Christian M. Meyer, Iryna Gurevych
Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps.
no code implementations • LREC 2012 • Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek, Christian M. Meyer
We present UBY-LMF, an LMF-based model for large-scale, heterogeneous multilingual lexical-semantic resources (LSRs).
no code implementations • LREC 2012 • Christian Chiarcos, Sebastian Hellmann, Sebastian Nordhoff, Steven Moran, Richard Littauer, Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek, Christian M. Meyer
This paper describes the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN).
no code implementations • ACL 2019 • Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael Sailer, Elisabeth Bauer, Martin R. Fischer, Frank Fischer, Iryna Gurevych
Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation.
no code implementations • IJCNLP 2019 • Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz, Jan Kiesewetter, Jan Zottmann, Michael Sailer, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data.
no code implementations • 23 Nov 2019 • Mohsen Mesgar, Paul Youssef, Lin Li, Dominik Bierwirth, Yihao Li, Christian M. Meyer, Iryna Gurevych
Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses.
no code implementations • LREC 2020 • Benjamin H{\"a}ttasch, Nadja Geisler, Christian M. Meyer, Carsten Binnig
Large state-of-the-art corpora for training neural networks to create abstractive summaries are mostly limited to the news genre, as it is expensive to acquire human-written summaries for other types of text at a large scale.
1 code implementation • ACL 2019 • Ji-Ung Lee, Erik Schwan, Christian M. Meyer
We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically.
1 code implementation • 26 Nov 2018 • Claudia Schulz, Christian M. Meyer, Michael Sailer, Jan Kiesewetter, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych
We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification.
1 code implementation • COLING 2016 • Christian M. Meyer, Judith Eckle-Kohler, Iryna Gurevych
We introduce the task of detecting cross-lingual marketing blunders, which occur if a trade name resembles an inappropriate or negatively connotated word in a target language.
1 code implementation • 7 Jun 2019 • Yang Gao, Christian M. Meyer, Iryna Gurevych
Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound.
1 code implementation • NAACL 2019 • Avinesh P. V. S, Christian M. Meyer
Neural sequence-to-sequence models have been successfully applied to text compression.
1 code implementation • COLING 2016 • Darina Benikova, Margot Mieskes, Christian M. Meyer, Iryna Gurevych
Coherent extracts are a novel type of summary combining the advantages of manually created abstractive summaries, which are fluent but difficult to evaluate, and low-quality automatically created extractive summaries, which lack coherence and structure.
1 code implementation • 30 Jul 2019 • Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych
The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards.
1 code implementation • ACL 2020 • Ji-Ung Lee, Christian M. Meyer, Iryna Gurevych
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training.
1 code implementation • LREC 2018 • Avinesh P. V. S., Maxime Peyrard, Christian M. Meyer
Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism.
1 code implementation • EMNLP 2018 • Yang Gao, Christian M. Meyer, Iryna Gurevych
The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries.
1 code implementation • COLING 2018 • Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych
To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
2 code implementations • IJCNLP 2019 • Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, Iryna Gurevych
Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.
7 code implementations • 13 Jun 2018 • Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych
To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
4 code implementations • IJCNLP 2019 • Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger
A robust evaluation metric has a profound impact on the development of text generation systems.