eger gurevych – Neural End

Steffen Eger, Johannes Daxenberger, Christian Stab, Iryna Gurevych. Association for Computational Linguistics, Santa Fe, NM, USA, pages 831-844. Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018).

Eger, Hoenen, Mehler. Language classification from bilingual word embedding graphs. In COLING 2016. Asgari and Mofrad. Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance. In: Workshop on Multilingual and Cross-lingual Methods in NLP, 2016.

Eger, Steffen; Youssef, Paul; Gurevych, Iryna (2018): Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks. Long Papers, In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, In: The 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31.10

Their combined citations are counted only for the first article. Merged citations. A Rücklé, S Eger, M Peyrard, I Gurevych. arXiv preprint arXiv:1803.01400, 2018. 38 * 2018: What is the essence of a claim? cross-domain claim identification. J Daxenberger, S Eger, I Habernal, C Stab, I Gurevych.

Jan 09, 2019 · Authors: Steffen Eger, Paul Youssef, Iryna Gurevych (Submitted on 9 Jan 2019) Abstract: Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning.

Cited by: 4
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Schnober, Carsten, and Eger, Steffen, and Do Dinh, Erik-Lan and Gurevych, Iryna. Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks.

(Schnober et al., 2016) ⇒ Carsten Schnober, Steffen Eger, Erik-Lan Do Dinh, and Iryna Gurevych. . “Still Not There? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks.” In: Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics

42 行 · Ubiquitous Knowledge Processing (UKP) Lab. Technische Universität Darmstadt Ubiquitous

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A ‘read’ is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

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Gurevych, 2015; Stab and Gurevych, 2017). Ac-cordingly, datasets typically differ with respect to their annotation of (often rather complex) argu-ment structure. Thus, feature sets would have to be manually adapted to and designed for each new sample of data, a challenging task. The same cri-tique applies to the designing of ILP constraints.

Nov 18, 2017 · Leonard Cohen cover. Live in Berlin, Nov 17 2017.

Steffen Eger Independent Research Group Leader, TU Darmstadt Verified email at aiphes.tu-darmstadt.de. Iryna Gurevych. Professor of Computer Science, Technische Universität Darmstadt. Verified email at cs.tu-darmstadt.de. natural language processing artificial intelligence semantics.

Chuck Yeager’s Advanced Flight Trainer was Electronic Art’s top selling game for 1987. In 2009, Yeager participated in the documentary The Legend of Pancho Barnes and the Happy Bottom Riding Club, a profile of his friend Pancho Barnes.

Battles/wars: World War II, Cold War, • Vietnam War
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Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. ( Eger et al., As Habernal and Gurevych [7]

Steffen Eger, Gözde Gül Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych: Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems.

Eger, Steffen; Youssef, Paul; Gurevych, Iryna (2018): Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks. Long Papers, In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, In: The 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31.10

Cited by: 6

Wei Zhao. He is a 2nd-year PhD student at Technische Universität Darmstadt, Germany, in the graudate school of AIPHES, working with Steffer Eger and Iryna Gurevych.He studied his master in Computer Science at University of Chinese Academy of Sciences, worked with Min Yang and Yu Qiao.He studied his bachelor in Linguistics at Fudan University.

Place where new submissions can be put before they are going to be moved to their final collection (which may need to be created before).

Iryna Gurevych (born on March 16, 1976 in Vinnytsia, Ukraine) is a Ukrainian computer scientist. She is Professor at the Department of Computer Science of the Technische Universität Darmstadt and Director of Ubiquitous Knowledge Processing Lab .

Steffen Eger, Gözde Gül ¸Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych.NAACL-HLT 2019. Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models.

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Multi-task learning is motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from related tasks by sharing parameters with other networks. However, humans do not consciously decide to transfer knowledge between tasks. In Natural Language Processing (NLP), it is hard to predict if sharing

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system

Cited by: 12
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the essays (Eger et al.,2017) and web discourse data (Habernal and Gurevych,2017) to the best approaches from the original publications. by enabling/disabling different labels without per-forming additional tagging runs. On the retrieval tab (cf. Figure3), the

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them.

Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych. Abstract Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an

Cited by: 12
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– Stab & Gurevych(2017) present a new approach for parsing argumentation structure. – Eger et al. (2018) extent the model from Stab & Gurevych(2017) to multilingual application. • Creating a Dutch essay corpus with argumentation structure annotated.

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[2] T. Miller, E.-L. Do Dinh, E. Simpson, and I. Gurevych. OFAI–UKP at [email protected]: Predicting the Humorousness of Tweets Using Gaus-sian Process Preference Learning.

@inproceedings{Schulz:2018:NAACL, title = {Multi-Task Learning for Argumentation Mining in Low-Resource Settings}, author = {Schulz, Claudia and Eger, Steffen and Daxenberger, Johannes and Kahse, Tobias and Gurevych, Iryna}, publisher = {Association for Computational Linguistics}, booktitle = {Proceedings of the 16th Annual Conference of the

Sallam Abualhaija, Tristan Miller, Judith Eckle-Kohler, Iryna Gurevych, and Karl-Heinz Zimmermann. Metaheuristic approaches to lexical substitution and simplification . In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017) , volume 1, pages 870–879, April 2017.

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Sep 22, 2017 · This is “Neural End-to-End Learning for Computational Argumentation Mining — Steffen Eger, Johannes Daxenberger and Iryna Gurevych” by ACL on Vimeo,

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A Rücklé, S Eger, M Peyrard, I Gurevych. arXiv preprint arXiv:1803.01400, 2018. 38 * 2018: What is the essence of a claim? cross-domain claim identification. J Daxenberger, S Eger, I Habernal, C Stab, I Gurevych. EMNLP 2017, 2017. 27: 2017: Multi-Task Learning for Argumentation Mining in Low-Resource Settings.

Steffen Eger Independent Research Group Leader, TU Darmstadt Verified email at aiphes.tu-darmstadt.de Iryna Gurevych Professor of Computer Science, Technische Universität Darmstadt Verified email at cs.tu-darmstadt.de

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Motivation •Growing interests in understanding peer-reviews •Assess rebuttal and author response [Gao, Eger, Kuznetsov, Gurevych, and Miyao, 2019]

Hey Iryna Gurevych! Claim your profile and join one of the world’s largest A.I. communities claim Claim with Google Claim with Twitter Claim with GitHub Claim with LinkedIn

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Eger, Steffen, Johannes Daxenberger, and Iryna Gurevych. 2017. Neural end-to-end learning for computational argumentation mining. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11 – 22, Vancouver. Crossref, Google Scholar: Elman, Jeffrey L. 1990. Finding structure in time.

Research interests. Digital Humanities; Supervised and weakly supervised machine learning; Metaphor and Non-Literal Language; Projects. Natur und Staat – Together with Alexandra Núnez (linguistics) and Malte Gerloff (philosophy) I have worked on the corpus “Natur und Staat” (1903-1911), a collection of “Preisschriften” concerning the interpretation of Darwin’s theories in political

The UKP ASPECT Corpus includes 3,595 sentence pairs over 28 controversial topics. The sentences were crawled from a large web crawl and identified as arguments for a given topic using the ArgumenText system. The sampling and matching of the sentence pairs is described in the paper. Then, the argument similarity annotation was done via crowdsourcing.

Iryna Gurevych (* 16. März 1976 in Winnyzja ) ist eine deutsche Informatikerin mit Schwerpunkt auf der Automatischen Sprachverarbeitung (NLP) . Sie gründete und leitet die Arbeitsgruppe Ubiquitous Knowledge Processing (UKP) am Fachbereich Informatik der TU Darmstadt .

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[2] T. Miller, E.-L. Do Dinh, E. Simpson, and I. Gurevych. OFAI–UKP at [email protected]: Predicting the Humorousness of Tweets Using Gaus-sian Process Preference Learning.

Steffen Eger, Johannes Daxenberger, Christian Stab, Iryna Gurevych Session 1-2-posters – HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation Sixing Wu 1 , Dawei Zhang 2 , Ying Li 3 , Xing Xie 2 , Zhonghai Wu 3

Aug 24, 2019 · Habernal, I., Gurevych, I.: Which argument is more convincing? Analyzing and predicting convincingness of web arguments using bidirectional LSTM. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), pp. 1589–1599 Google Scholar

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We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state

Mar 04, 2018 · 4 Mar 2018 • Andreas Rücklé • Steffen Eger • Maxime Peyrard • Iryna Gurevych Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models such as InferSent. ..

”Our working group has well and visibly established the TU in the field of argumentmining.“ says Professor Iryna Gurevych, head of the UKP. For this purpose, the interdisciplinary team works with the TU Department of Social and Historical Sciences, as well as with other universities from the network of Rhine-Main universities.

Welcome! For the first time, the program page will allow conference attendees to choose the sessions (and individual papers and posters) they want to attend and generate a PDF of their customized schedule!. This page should work on modern browsers on all operating systems (Internet Explorer <= v10 will likely not work).

– Steffen Eger, Johannes Daxenberger, Christian Stab and Iryna Gurevych. Deep Enhanced Representation for Implicit Discourse Relation Recognition – Hongxiao Bai and Hai Zhao. Dependent Gated Reading for Cloze-Style Question Answering – Reza Ghaeini, Xiaoli Fern, Hamed Shahbazi and Prasad Tadepalli.

Steffen Eger is this you? claim profile. 0 followers IG Farben Haus Featured Co-authors. Iryna Gurevych 55 publications . Chao Li 54 publications . Yang Gao 52 publications . Erik Cambria 35 publications . Fei Liu 33 publications

Ackermans, K., Rusman, E., Brand-Gruwel, S., & Specht, M. (2016, October). A first step towards synthesizing rubrics and video for the formative assessment of complex

Steffen Eger, Gözde Gül ¸Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych.NAACL-HLT 2019. Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models.

Nov 22, 2019 · 日本の組織を所属とする者が、2019年に言語処理のトップカンファレンスもしくはトップ論文誌で発表した論文一覧です (2018年版、2017年版、2016年版。2015年版、2014年版は Graham Neubig さん (NAIST、現 CMU) が作成)。 対象は TACL、NAACL、ACL、EMNLP です。

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tated Essay Corpus (Stab and Gurevych,2014a; Stab,2017), which is studied byPersing and Ng (2016),Eger et al.(2017),Stab(2017) himself, and also by us. However, for a unit segmentation al-gorithm to be integrated into applications, it has to work robustly also for new texts from other do-mains. This paper therefor extends the discussion

P17-4004 : Andreas Rücklé; Iryna Gurevych End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights. P17-4005 : Dustin Arendt; Svitlana Volkova ESTEEM: A Novel Framework for Qualitatively Evaluating

ArgumenText: Searching for Arguments in Heterogeneous Sources — Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych – Ubiquitous Knowledge Processing Lab, Department of Computer Science, Technische Universität Darmstadt, Germany

Abstract. Argument Mining has become a remarkable research area in computational argumentation and Natural Language Processing fields. Despite its importance, most of the current proposals are restricted to a text type (e.g., Essays, web comments) on a specific domain and fall behind expectations when applied to cross-domain data.

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[Stab and Gurevych, 2017] • End-to-End AM System [Eger et al., 2017] • Encoder-decoder formulation employing a pointer network [Potash et al., 2017] • Discourse Parsing –NN architecture: Sentence Encoding using word embeddings + lexical + syntactic info)

Beatrice Alex, Stefania Degaetano-Ortlieb, Anna Feldman, Anna Kazantseva, Nils Reiter, Stan Szpakowicz: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, [email protected] 2018, Santa Fe, New Mexico, USA, August 25, 2018.

Andreas Rücklé, Krishnkant Swarnkar, Iryna Gurevych WWW 2019 (now called The WebConf’19) [h5-index: 76] PDF | Bibtex. Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems Steffen Eger, Gozde Gul Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych

Jul 22, 2019 · Yang Gao, Steffen Eger, Ilia Kuznetsov, Iryna Gurevych and Yusuke Miyao. So, instead of author response, we decided to invest in promoting discussion within the PC, and on ensuring that discussions, papers and reviews have the full attention of ACs.