Roxanne El Baff | Bauhaus-University-Weimar (original) (raw)

Papers by Roxanne El Baff

Research paper thumbnail of Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2023

Graphs are a natural representation of complex data as their structure allows users to discover (... more Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. To account for this, we present the Corpus Annotation Graph (CAG) architectural framework based on a create-and-annotate pattern that enables users to build uniformly structured graphs from diverse data sources and extend them with automatically extracted annotations (e.g., named entities, topics). The resulting graphs can be used for further analyses across multiple downstream tasks (e.g., node classification). Code and resources are publicly available on GitHub, and downloadable via PyPi with the command {texttt{pip install cag}.

Research paper thumbnail of Computational Argumentation Synthesis as a Language Modeling Task

Proceedings of the 12th International Conference on Natural Language Generation

Synthesis approaches in computational argumentation so far are restricted to generating claim-lik... more Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates. Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations. In this paper, we approach such an argumentation synthesis as a language modeling task. In our language model, argumentative discourse units are the "words", and arguments represent the "sentences". Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units' argumentative roles, and finally "phrases" an argument by instantiating the structure with semantically coherent units from the pool. Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.

Research paper thumbnail of Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

Proceedings of the 22nd Conference on Computational Natural Language Learning

News editorials are said to shape public opinion, which makes them a powerful tool and an importa... more News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone's stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial's stance-or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators' political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone's stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.

Research paper thumbnail of Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2023

Graphs are a natural representation of complex data as their structure allows users to discover (... more Graphs are a natural representation of complex data as their structure allows users to discover (often implicit) relations among the nodes intuitively. Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. To account for this, we present the Corpus Annotation Graph (CAG) architectural framework based on a create-and-annotate pattern that enables users to build uniformly structured graphs from diverse data sources and extend them with automatically extracted annotations (e.g., named entities, topics). The resulting graphs can be used for further analyses across multiple downstream tasks (e.g., node classification). Code and resources are publicly available on GitHub, and downloadable via PyPi with the command {texttt{pip install cag}.

Research paper thumbnail of Computational Argumentation Synthesis as a Language Modeling Task

Proceedings of the 12th International Conference on Natural Language Generation

Synthesis approaches in computational argumentation so far are restricted to generating claim-lik... more Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates. Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations. In this paper, we approach such an argumentation synthesis as a language modeling task. In our language model, argumentative discourse units are the "words", and arguments represent the "sentences". Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units' argumentative roles, and finally "phrases" an argument by instantiating the structure with semantically coherent units from the pool. Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.

Research paper thumbnail of Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

Proceedings of the 22nd Conference on Computational Natural Language Learning

News editorials are said to shape public opinion, which makes them a powerful tool and an importa... more News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone's stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial's stance-or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators' political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1% of all editorials changed anyone's stance, more than 5% meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.