Neural Word Decomposition Models for Abusive Language Detection
| Autor: | Yaser Al-Onaizan, Sravan Bodapati, Kasturi Bhattacharjee, Spandana Gella |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Byte pair encoding Computer Science - Computation and Language Language identification Computer science business.industry computer.software_genre Robustness (computer science) Social media Artificial intelligence Language model business Computation and Language (cs.CL) computer Natural language processing |
| Popis: | User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc. that are targeted to attack or abuse a specific group of people. Often such text is written differently compared to traditional text such as news involving either explicit mention of abusive words, obfuscated words and typological errors or implicit abuse i.e., indicating or targeting negative stereotypes. Thus, processing this text poses several robustness challenges when we apply natural language processing techniques developed for traditional text. For example, using word or token based models to process such text can treat two spelling variants of a word as two different words. Following recent work, we analyze how character, subword and byte pair encoding (BPE) models can be aid some of the challenges posed by user generated text. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with finetuning large pretrained language models, and demonstrate their robustness to domain shift by studying Wikipedia attack, toxicity and Twitter hatespeech datasets Accepted at ALW Workshop at ACL2019, Florence; BERT has a WordPiece model and it enhances performance of word based models in noisy settings |
| Databáze: | OpenAIRE |
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