Allen Roush


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Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio
Allen Roush | Sanjay Basu | Akshay Moorthy | Dmitry Dubovoy
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI

Despite rapid advancement in the field of Constrained Natural Language Generation, little time has been spent on exploring the potential of language models which have had their vocabularies lexically, semantically, and/or phonetically constrained. We find that most language models generate compelling text even under significant constraints. We present a simple and universally applicable technique for modifying the output of a language model by compositionally applying filter functions to the language models vocabulary before a unit of text is generated. This approach is plug-and-play and requires no modification to the model. To showcase the value of this technique, we present an easy to use AI writing assistant called “Constrained Text Generation Studio” (CTGS). CTGS allows users to generate or choose from text with any combination of a wide variety of constraints, such as banning a particular letter, forcing the generated words to have a certain number of syllables, and/or forcing the words to be partial anagrams of another word. We introduce a novel dataset of prose that omits the letter “e”. We show that our method results in strictly superior performance compared to fine-tuning alone on this dataset. We also present a Huggingface “space” web-app presenting this technique called Gadsby. The code is available to the public here:


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DebateSum: A large-scale argument mining and summarization dataset
Allen Roush | Arvind Balaji
Proceedings of the 7th Workshop on Argument Mining

Prior work in Argument Mining frequently alludes to its potential applications in automatic debating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to problems found within competitive formal debate. To remedy this, we present the DebateSum dataset. DebateSum consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries. DebateSum was made using data compiled by competitors within the National Speech and Debate Association over a 7year period. We train several transformer summarization models to benchmark summarization performance on DebateSum. We also introduce a set of fasttext word-vectors trained on DebateSum called debate2vec. Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today. The DebateSum search engine is available to the public here: