Search Results for author:
Found 10 papers, 4 papers with code
TACAM: Topic And Context Aware Argument Mining
In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them.
Using Supervised Learning to Classify Metadata of Research Data by Discipline of Research
Both applications perform at scale with the proposed models which are available for re-use.
Unsupervised Anomaly Detection for X-Ray Images
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
Diversity Aware Relevance Learning for Argument Search
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
Argument Mining Driven Analysis of Peer-Reviews
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
Active Learning for Argument Strength Estimation
High-quality arguments are an essential part of decision-making.
Conifer Seedling Detection in UAV-Imagery with RGB-Depth Information
Increasing the resolution of this height information also showed little effect.
Towards a Holistic View on Argument Quality Prediction
While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.
Tokenizer Choice For LLM Training: Negligible or Crucial?
The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?
The adaption of multilingual pre-trained Large Language Models (LLMs) into eloquent and helpful assistants is essential to facilitate their use across different language regions.
