Search Results for author: Dietrich Trautmann

Found 8 papers, 3 papers with code

Large Language Model Prompt Chaining for Long Legal Document Classification

no code implementations8 Aug 2023 Dietrich Trautmann

Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.

Document Classification In-Context Learning +2

Legal Prompt Engineering for Multilingual Legal Judgement Prediction

no code implementations5 Dec 2022 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.

Language Modelling Large Language Model +1

Active Learning for Argument Mining: A Practical Approach

no code implementations28 Sep 2021 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 Argument Mining

Aspect-Based Argument Mining

1 code implementation COLING (ArgMining) 2020 Dietrich Trautmann

In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS).

Argument Mining Term Extraction

Multipurpose Intelligent Process Automation via Conversational Assistant

no code implementations7 Jan 2020 Alena Moiseeva, Dietrich Trautmann, Michael Heimann, Hinrich Schütze

Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i. e., customer support).

Transfer Learning

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

no code implementations NAACL 2019 Luisa März, Dietrich Trautmann, Benjamin Roth

We propose an architecture that trains an out-of-domain model on a large newswire corpus, and transfers those weights by using them as a prior for a model trained on the target domain (a data-set of German Tweets) for which there is very little an-notations available.

Domain Adaptation Part-Of-Speech Tagging +3

Sequence Labeling: A Practical Approach

1 code implementation12 Aug 2018 Adnan Akhundov, Dietrich Trautmann, Georg Groh

We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources.

Chunking NER +2

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