Sravan Bodapati author Spandana Gella author Kasturi Bhattacharjee author Yaser Al-Onaizan author 2019-08 text Sarah T Roberts editor Joel Tetreault editor Vinodkumar Prabhakaran editor Zeerak Waseem editor Association for Computational Linguistics Florence, Italy conference publication The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. 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 recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset. bodapati-etal-2019-neural 10.18653/v1/W19-3515 https://aclanthology.org/W19-3515 2019-08 135 145