Papers by Tristan Miller
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. |
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning (P19-1)
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| Challenge: | Inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding. |
| Approach: | They propose a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning (GPPL) . |
| Outcome: | The proposed approach achieves a Spearman’s of 0.56 against gold using word embeddings and linguistic features. |
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
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| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |