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Found 15 papers, 8 papers with code
The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines
Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document.
A Framework for Monitoring and Retraining Language Models in Real-World Applications
In the Machine Learning (ML) model development lifecycle, training candidate models using an offline holdout dataset and identifying the best model for the given task is only the first step.
Biomedical Concept Relatedness -- A large EHR-based benchmark
We open-source a novel concept relatedness benchmark overcoming these issues: it is six times larger than existing datasets and concept pairs are chosen based on co-occurrence in EHRs, ensuring their relevance for the application of interest.
Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!
The novel datasets thus form a challenging new benchmark for the development of medical embeddings able to accurately represent the whole medical terminology.
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web.
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains
Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation.
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comments in social media (e. g., "! d10t") or as a writing style ("1337" in "leet speak"), among other scenarios.
Challenges in the Automatic Analysis of Students' Diagnostic Reasoning
We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification.
Answering the "why" in Answer Set Programming - A Survey of Explanation Approaches
Artificial Intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.
Multi-Task Learning for Argumentation Mining in Low-Resource Settings
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.
Assessing Convincingness of Arguments in Online Debates with Limited Number of Features
We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016).
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia).
Justifying Answer Sets using Argumentation
An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not.
