activity
20182022
most citedLegal Prompt Engineering for Multilingual Legal Judgement Prediction

31 citations · 32 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL202231 cited

Legal Prompt Engineering for Multilingual Legal Judgement Prediction

Dietrich Trautmann, Alina Petrova, Frank Schilder

Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Ou…

cs.CL2021

Active Learning for Argument Mining: A Practical Approach

Nikolai Solmsdorf, Dietrich Trautmann, Hinrich Schütze

Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining. Active Learning reduces…

cs.CL2020

Aspect-Based Argument Mining

Dietrich Trautmann

Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to so…

cs.CL2020

Multipurpose Intelligent Process Automation via Conversational Assistant

Alena Moiseeva, Dietrich Trautmann, Michael Heimann +1

Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Con…

cs.CL20191 cited

Domain adaptation for part-of-speech tagging of noisy user-generated text

Luisa März, Dietrich Trautmann, Benjamin Roth

The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data availabl…

cs.CL2019

Fine-Grained Argument Unit Recognition and Classification

Dietrich Trautmann, Johannes Daxenberger, Christian Stab +2

Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both fr…