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Hate speech is an important problem in the management of user-generated content. In order to remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on transformer architecture, such as (multilingual) BERT model, achieve superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo Dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate the introduced approach on hate speech detection problems in several languages. Our approach not only improves the classification performance of the state-of-the-art multilingual BERT model but the computed reliability scores also significantly reduce the workload in inspection of offending cases and in reannotation campaigns.
", "funding": [ { "award": { "acronym": "EMBEDDIA", "id": "00k4n6c32::825153", "identifiers": [ { "identifier": "https://cordis.europa.eu/projects/825153", "scheme": "url" } ], "number": "825153", "program": "H2020", "title": { "en": "Cross-Lingual Embeddings for Less-Represented Languages in European News Media" } }, "funder": { "id": "00k4n6c32", "name": "European Commission" } } ], "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2022-03-28", "publisher": "Zenodo", "resource_type": { "id": "publication-conferencepaper", "title": { "de": "Konferenzbeitrag", "en": "Conference paper" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "title": "Bayesian BERT for Trustful Hate Speech Detection" }, "parent": { "access": { "owned_by": { "user": 71235 } }, "communities": { "default": "dea49b1c-5a94-46cd-8b11-1d91046d2aba", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2019-07-02T19:53:01.532358+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "dea49b1c-5a94-46cd-8b11-1d91046d2aba", "links": {}, "metadata": { "curation_policy": "", "page": "The EMBEDDIA project seeks to address these challenges by leveraging innovations in the use of cross-lingual embeddingscoupled with deep neural networks to allow existing monolingual resources to be used across languages, leveraging their high speed of operation for near real-time applications, without the need for large computational resources. Across three years, the project’s six academic and four industry partners will develop novel solutions including for under-represented languages, and test them in real-world news and media production contexts.
", "title": "EMBEDDIA - Cross-Lingual Embeddings for Less-Represented Languages in European News Media" }, "revision_id": 0, "slug": "embeddia", "updated": "2019-07-02T20:45:20.131421+00:00" }, { "access": { "member_policy": "open", "members_visibility": "restricted", "record_policy": "open", "review_policy": "closed", "visibility": "public" }, "children": { "allow": true }, "created": "2022-11-23T15:53:29.436323+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "f0a8b890-f97a-4eb2-9eac-8b8a712d3a6c", "links": {}, "metadata": { "curation_policy": "The EU Open Research Repository serves as a repository for research outputs (data, software, posters, presentations, publications, etc) which have been funded under an EU research funding programme such as Horizon Europe, Euratom or earlier Framework Programmes.
\nThe community is managed by CERN on behalf of the European Commission.
\nZenodo’s general policies and Terms of Use apply to all content.
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", "description": "Open repository for EU-funded research outputs from Horizon Europe, Euratom and earlier Framework Programmes.", "organizations": [ { "id": "00k4n6c32" } ], "page": "The EU Open Research Repository is a Zenodo-community dedicated to fostering open science and enhancing the visibility and accessibility of research outputs funded by the European Union. The community is managed by CERN on behalf of the European Commission.
\nThe mission of the repository is to support the implementation of the EU's open science policy, providing a trusted and comprehensive space for researchers to share their research outputs such as data, software, reports, presentations, posters and more. The EU Open Research Repository simplifies the process of complying with open science requirements, ensuring that research outputs from Horizon Europe, Euratom, and earlier Framework Programmes are freely accessible, thereby accelerating scientific discovery and innovation.
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