Papers by Christian Stab
Classification and Clustering of Arguments with Contextualized Word Embeddings (P19-1)
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| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
| Approach: | They propose to use contextualized word embeddings to classify and cluster topic-dependent arguments using a UKP Sentential Argument Mining Corpus and IBM Debater - Evidence Sentences datasets. |
| Outcome: | The proposed method improves state-of-the-art on argument classification and clustering tasks and across multiple datasets. |
ArgumenText: Searching for Arguments in Heterogeneous Sources (N18-5)
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Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
| Challenge: | Argument mining is a core technology for enabling argument search in large corpora . but current methods fail when applied to heterogeneous texts . despite its obvious applications, argument search has attracted relatively little attention . |
| Approach: | They propose a system that searches sentential arguments for any given topic . ArgumenText automatically identifies and classifies arguments by relevance . |
| Outcome: | The proposed system covers 89% of arguments found in expert-curated lists . it also identifies additional valid arguments omitted or overlooked by human curators . |
Cross-topic Argument Mining from Heterogeneous Sources (D18-1)
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| Challenge: | Argument mining is a core technology for automating argument search in document collections. |
| Approach: | They propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. |
| Outcome: | The proposed scheme outperforms vanilla BiLSTMs in two- and three-label cross-topic settings and can be further improved by leveraging additional data for topic relevance using multi-task learning. |
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! (C18-1)
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| Challenge: | Argumentation mining (AM) requires the identification of complex discourse structures . existing resources are not adequate for assessing cross-lingual AM due to their heterogeneity or lack of complexity. |
| Approach: | They propose to use a dataset to translate persuasive student essays into German, French, Spanish, and Chinese to compare arguments mining and annotation projection. |
| Outcome: | The proposed methods perform better when using expensive human or cheap machine translations and almost eliminate loss from cross-lingual transfer. |