Style Obfuscation by Invariance

The task of obfuscating writing style using sequence models has previously been investigated under the framework of obfuscation-by-transfer, where the input text is explicitly rewritten in another style. These approaches also often lead to major alterations to the semantic content of the input. In this work, we propose obfuscation-by-invariance, and investigate to what extent models trained to be explicitly style-invariant preserve semantics. We evaluate our architectures on parallel and non-parallel corpora, and compare automatic and human evaluations on the obfuscated sentences. Our experiments show that style classifier performance can be reduced to chance level, whilst the automatic evaluation of the output is seemingly equal to models applying style-transfer. However, based on human evaluation we demonstrate a trade-off between the level of obfuscation and the observed quality of the output in terms of meaning preservation and grammaticality.Comment: Accepted for presentation at COLING1

Source-driven Representations for Hate Speech Detection

Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Polarized distributed representations created over such content prove superior to generic embeddings in the task of hate speech detection. The same content seems to carry a too weak signal to proxy silver labels in a distant supervised setting. However, this signal is stronger than gold labels which come from a different distribution, leading to re-think the process of annotation in the context of highly subjective judgments.La provenienza di ciò che viene condiviso su Facebook costituisce un primo elemento indentificativo di contentuti carichi di odio. La rappresentazione distribuita polarizzata che costruiamo su tali contenuti si dimostra migliore nell’individuazione di argomenti di odio rispetto ad alternative più generiche. Il potere predittivo di tali embedding polarizzati risulta anche più incisivo rispetto a quello di dati gold standard che sono caratterizzati da una distribuzione ed una annotatione diverse

Source-driven Representations for Hate Speech Detection

Sources, in the form of selected Facebook pages, can be used as indicators of hate-rich content. Polarized distributed representations created over such content prove superior to generic embeddings in the task of hate speech detection. The same content seems to carry a too weak signal to proxy silver labels in a distant supervised setting. However, this signal is stronger than gold labels which come from a different distribution, leading to re-think the process of annotation in the context of highly subjective judgments.La provenienza di ciò che viene condiviso su Facebook costituisce un primo elemento indentificativo di contentuti carichi di odio. La rappresentazione distribuita polarizzata che costruiamo su tali contenuti si dimostra migliore nell’individuazione di argomenti di odio rispetto ad alternative più generiche. Il potere predittivo di tali embedding polarizzati risulta anche più incisivo rispetto a quello di dati gold standard che sono caratterizzati da una distribuzione ed una annotatione diverse

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges