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Training argumentation skills with argumentative writing support
Stab, Christian; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Petukhova, Volha; Tian, Ye (Hrsg.): Proceedings of the 21st Workshop on the Semantics and Pragmatics Dialogue (SEMDIAL 2017 SaarDial), Saarbrücken, 15-17 August 2017 | Saarbrücken: Saarland Universität | 2017
37873 Endnote
Autor*innen:
Stab, Christian; Gurevych, Iryna
Titel:
Training argumentation skills with argumentative writing support
Aus:
Petukhova, Volha; Tian, Ye (Hrsg.): Proceedings of the 21st Workshop on the Semantics and Pragmatics Dialogue (SEMDIAL 2017 SaarDial), Saarbrücken, 15-17 August 2017, Saarbrücken: Saarland Universität, 2017 (Proceedings (SemDial)), S. 174-175
URL:
www.saardial.uni-saarland.de/wordpress/wp-content/uploads/SemDial2017SaarDial_proceedings.pdf#page=182
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Argumentation; Computerlinguistik; Textanalyse
Abstract:
We present an writing support system for assessing written arguments. Our system incorporates three analysis models allowing for rich feedback about argumentation structure, quality of reasons, and presence of opposing arguments. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Integrating deep linguistics features in factuality prediction over unified datasets
Stanovsky, Gabriel; Eckle-Kohler, Judith; Puzikov, Yevgeniy; Dagan, Ido; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017
37874 Endnote
Autor*innen:
Stanovsky, Gabriel; Eckle-Kohler, Judith; Puzikov, Yevgeniy; Dagan, Ido; Gurevych, Iryna
Titel:
Integrating deep linguistics features in factuality prediction over unified datasets
Aus:
Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 352-357
DOI:
10.18653/v1/P17-2056
URL:
https://aclanthology.info/pdf/P/P17/P17-2056.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Modell; Methode; Daten; Sprache; Wort; Semantik
Abstract:
Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Argumentation quality assessment. Theory vs. practice
Wachsmuth, Henning; Naderi, Nona; Habernal, Ivan; Hou, Yufang; Hirst, Graeme; Gurevych, Iryna; […]
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017. Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017
37875 Endnote
Autor*innen:
Wachsmuth, Henning; Naderi, Nona; Habernal, Ivan; Hou, Yufang; Hirst, Graeme; Gurevych, Iryna; Stein, Benno
Titel:
Argumentation quality assessment. Theory vs. practice
Aus:
Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017. Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 250-255
DOI:
10.18653/v1/P17-2039
URL:
https://aclanthology.info/pdf/P/P17/P17-2039.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Argumentation; Diskussion; Qualität; Bewertung; Theorie; Praxis; Vergleich
Abstract:
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Generating training data for semantic role labeling based on label transfer from linked lexical […]
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Zeitschriftenbeitrag
| In: Transactions of the Association for Computational Linguistics | 2016
36232 Endnote
Autor*innen:
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Titel:
Generating training data for semantic role labeling based on label transfer from linked lexical resources
In:
Transactions of the Association for Computational Linguistics, (2016)
URL:
https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/717
Dokumenttyp:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache:
Englisch
Schlagwörter:
Ambiguität; Automatisierung; Computerlinguistik; Computerunterstütztes Verfahren; Semantik; Textanalyse; Wort; Wörterbuch
Abstract (english):
We present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., WordNet, FrameNet, Wiktionary) by linking them on the sense or on the role level. Unlike resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses and roles. The automatically labeled training data (http://www.ukp.tu-darmstadt.de/knowledge-based-srl/) are evaluated on four corpora from different domains for the tasks of word sense disambiguation and semantic role classification. Results show that classifiers trained on our generated data equal those resulting from a standard supervised setting. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Automatic coding of short text responses via clustering in educational assessment
Zehner, Fabian; Sälzer, Christine; Goldhammer, Frank
Zeitschriftenbeitrag
| In: Educational and Psychological Measurement | 2016
35473 Endnote
Autor*innen:
Zehner, Fabian; Sälzer, Christine; Goldhammer, Frank
Titel:
Automatic coding of short text responses via clustering in educational assessment
In:
Educational and Psychological Measurement, 76 (2016) 2, S. 280-303
DOI:
10.1177/0013164415590022
URN:
urn:nbn:de:0111-pedocs-149795
URL:
https://nbn-resolving.org/urn:nbn:de:0111-pedocs-149795
Dokumenttyp:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache:
Englisch
Schlagwörter:
Antwort; Automatisierung; Codierung; Computerlinguistik; Leistungstest; Methode; PISA <Programme for International Student Assessment>; Software; Technologiebasiertes Testen; Testkonstruktion
Abstract:
Automatic coding of short text responses opens new doors in assessment. We implemented and integrated baseline methods of natural language processing and statistical modelling by means of software components that are available under open licenses. The accuracy of automatic text coding is demonstrated by using data collected in the Programme for International Student Assessment (PISA) 2012 in Germany. Free text responses of 10 items with Formula responses in total were analyzed. We further examined the effect of different methods, parameter values, and sample sizes on performance of the implemented system. The system reached fair to good up to excellent agreement with human codings Formula Especially items that are solved by naming specific semantic concepts appeared properly coded. The system performed equally well with Formula and somewhat poorer but still acceptable down to Formula Based on our findings, we discuss potential innovations for assessment that are enabled by automatic coding of short text responses. (DIPF/Orig.)
DIPF-Abteilung:
Bildungsqualität und Evaluation
Argumentation. Content, structure, and relationship with essay quality
Beigman Klebanov, Beata; Stab, Christian; Song, Yi; Gyawali, Binod; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016) | Stroudsburg; PA: Association for Computational Linguistics | 2016
36977 Endnote
Autor*innen:
Beigman Klebanov, Beata; Stab, Christian; Song, Yi; Gyawali, Binod; Gurevych, Iryna
Titel:
Argumentation. Content, structure, and relationship with essay quality
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016), Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 70-75
URL:
http://aclweb.org/anthology/W/W16/W16-2808.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Argumentation; Aufsatz; Computerlinguistik; Evaluation; Experiment; Inhalt; Qualität; Struktur
Abstract (english):
In this paper, we investigate the relationship between argumentation structures and (a) argument content, and (b) the holistic quality of an argumentative essay. Our results suggest that structure-based approaches hold promise for automated evaluation of argumentative writing. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Supersense embeddings. A unified model for supersense interpretation, prediction and utilization
Flekova, Lucie; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016): Long Papers | Stroudsburg; PA: Association for Computational Linguistics | 2016
36971 Endnote
Autor*innen:
Flekova, Lucie; Gurevych, Iryna
Titel:
Supersense embeddings. A unified model for supersense interpretation, prediction and utilization
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016): Long Papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 2029-2041
URL:
http://www.aclweb.org/anthology/P16-1191
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Beziehung; Computerlinguistik; Klassifikation; Semantik; Sinn; Wort
Abstract (english):
Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using […]
Habernal, Ivan; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers | Stroudsburg; PA: Association for Computational Linguistics | 2016
36970 Endnote
Autor*innen:
Habernal, Ivan; Gurevych, Iryna
Titel:
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 1589-1599
URL:
http://www.aclweb.org/anthology/P16-1150
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Argumentation; Automatisierung; Computerlinguistik; Kommunikation; Online; Prognose; Qualität; Rhetorik; Soziale Software; Textanalyse; Überzeugung; World wide web 2.0
Abstract (english):
We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman's correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
What makes word-level neural machine translation hard. A case study on English-German translation
Hirschmann, Fabian; Nam, Jinseok; Fürnkranz, Johannes
Sammelbandbeitrag
| Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) | Osaka: The COLING 2016 Organizing Committee | 2016
36983 Endnote
Autor*innen:
Hirschmann, Fabian; Nam, Jinseok; Fürnkranz, Johannes
Titel:
What makes word-level neural machine translation hard. A case study on English-German translation
Aus:
The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 3199-3208
URL:
http://aclweb.org/anthology/C/C16/C16-1301.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Computerunterstütztes Verfahren; Deutsch; Englisch; Syntax; Übersetzung; Wort; Wörterbuch
Abstract (english):
Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT'14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Modeling extractive sentence intersection via subtree entailment
Levy, Omer; Dagan, Ido; Stanovsky, Gabriel; Eckle-Kohler, Judith; Gurevych, Iryna
Sammelbandbeitrag
| Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) | Osaka: The COLING 2016 Organizing Committee | 2016
36987 Endnote
Autor*innen:
Levy, Omer; Dagan, Ido; Stanovsky, Gabriel; Eckle-Kohler, Judith; Gurevych, Iryna
Titel:
Modeling extractive sentence intersection via subtree entailment
Aus:
The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 2891-2901
URL:
http://www.aclweb.org/anthology/C/C16/C16-1272.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Computerlinguistik; Daten; Klassifikation; Semantik; Struktur; Syntax; Text
Abstract (english):
Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
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