ERRANT extracts the errors and classifies them into error types, in the form of an edit that can be used in the creation of GEC systems, as well as for grammatical error analysis.
We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible.
We introduce a new task, context-sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered.
In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT.
The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information.
The potential of the presented resources is investigated by detecting and studying the emotion of `disgust' in the Greek Parliament records.
Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history.
Ranked #1 on Ancient Text Restoration on I.PHI
User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets.
For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans.
Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts.
Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination.
We show that a neural language model can achieve as high as 51. 3% accuracy in radiology reports (one out of two words predicted correctly).
This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam.
This paper presents the application of two strong baseline systems for toxicity detection and evaluates their performance in identifying and categorizing offensive language in social media.
Experimenting with a new dataset of 1. 6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams.
Experimenting with a dataset of approximately 1. 6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases.