Search Results for author: Christian M. Meyer

Found 29 papers, 16 papers with code

Empowering Active Learning to Jointly Optimize System and User Demands

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.

Active Learning

Summarization Beyond News: The Automatically Acquired Fandom Corpora

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.

Abstractive Text Summarization

When is ACL's Deadline? A Scientific Conversational Agent

no code implementations23 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.

Better Rewards Yield Better Summaries: Learning to Summarise Without References

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.

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

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.

Multiple-choice

Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

1 code implementation30 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.

Decision Making Learning-To-Rank +1

Manipulating the Difficulty of C-Tests

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.

Preference-based Interactive Multi-Document Summarisation

1 code implementation7 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.

Active Learning reinforcement-learning

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

1 code implementation26 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.

A Retrospective Analysis of the Fake News Challenge Stance-Detection Task

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.

General Classification Stance Classification +1

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

7 code implementations13 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.

General Classification Stance Classification +1

Live Blog Corpus for Summarization

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.

Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback

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.

Active Learning Document Summarization +1

Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sources

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.

Document Summarization Multi-Document Summarization

Semi-automatic Detection of Cross-lingual Marketing Blunders based on Pragmatic Label Propagation in Wiktionary

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.