Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations.
The task of Argument Mining, that is extracting argumentative sentences for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argumentative sentences requires expert knowledge.
Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.
We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other languages.
We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments.
In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect.
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually.
Argument mining is a core technology for enabling argument search in large corpora.
We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification.
To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.
Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.