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Found 39 papers, 4 papers with code
CEHA: A Dataset of Conflict Events in the Horn of Africa
Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict.
Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election
Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities.
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid
Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals.
A New Task and Dataset on Detecting Attacks on Human Rights Defenders
The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events.
Supporting Spanish Writers using Automated Feedback
We present a tool that provides automated feedback to students studying Spanish writing.
Don't take ``nswvtnvakgxpm'' for an answer --The surprising vulnerability of automatic content scoring systems to adversarial input
Automatic content scoring systems are widely used on short answer tasks to save human effort.
Using PRMSE to evaluate automated scoring systems in the presence of label noise
The effect of noisy labels on the performance of NLP systems has been studied extensively for system training.
Context-based Automated Scoring of Complex Mathematical Responses
The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately.
Automated Scoring: Beyond Natural Language Processing
In this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying the latest machine learning techniques to publicly available data in order to obtain the best accuracy.
Atypical Inputs in Educational Applications
In large-scale educational assessments, the use of automated scoring has recently become quite common.
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks
We consider the automatic scoring of a task for which both the content of the response as well its spoken fluency are important.
A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring
We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230, 000 responses scored by human raters.
Investigating neural architectures for short answer scoring
Neural approaches to automated essay scoring have recently shown state-of-the-art performance.
A Report on the 2017 Native Language Identification Shared Task
We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks.
Building Better Open-Source Tools to Support Fairness in Automated Scoring
Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®.
Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring
Developments in the educational landscape have spurred greater interest in the problem of automatically scoring short answer questions.
