Presents a new expert-curated dataset of multi-turn counterspeech dialogues in five languages targeting hate against seven groups, with span annotations linking to verified external knowledge for RAG applications.
arXiv preprint arXiv:2002.06823 , year=
5 Pith papers cite this work, alongside 173 external citations. Polarity classification is still indexing.
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abstract
The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks enough exploration. While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning. This motivates us to think how to better leverage BERT for NMT along this direction. We propose a new algorithm named BERT-fused model, in which we first use BERT to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the NMT model through attention mechanisms. We conduct experiments on supervised (including sentence-level and document-level translations), semi-supervised and unsupervised machine translation, and achieve state-of-the-art results on seven benchmark datasets. Our code is available at \url{https://github.com/bert-nmt/bert-nmt}.
years
2026 5representative citing papers
LLMs generate adequate counterspeech for co-occurring hate and misinformation in 40% of cases, with a mixed knowledge strategy from fact-checkers and NGOs proving most effective after expert revision.
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
A new Romanian GEC corpus of 10k pairs plus pretraining a Transformer on artificial errors generated via POS tagger yields F0.5 of 53.76, beating the 44.38 baseline from training only on the corpus.
Self-critical policy-gradient training of a multi-product BERT MLM produces ad headlines that beat LSTM+RL baselines and human submissions on overlap metrics and blind quality/grammar audits.
citing papers explorer
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CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges
Presents a new expert-curated dataset of multi-turn counterspeech dialogues in five languages targeting hate against seven groups, with span annotations linking to verified external knowledge for RAG applications.
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Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation
LLMs generate adequate counterspeech for co-occurring hate and misinformation in 40% of cases, with a mixed knowledge strategy from fact-checkers and NGOs proving most effective after expert revision.
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BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
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Neural Grammatical Error Correction for Romanian
A new Romanian GEC corpus of 10k pairs plus pretraining a Transformer on artificial errors generated via POS tagger yields F0.5 of 53.76, beating the 44.38 baseline from training only on the corpus.
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Ad Headline Generation using Self-Critical Masked Language Model
Self-critical policy-gradient training of a multi-product BERT MLM produces ad headlines that beat LSTM+RL baselines and human submissions on overlap metrics and blind quality/grammar audits.