text: 388,90,1319458053629116418,112654363,Torsten Scholak,"Our new text-to-SQL paper is now on the ArXiv! <LINK> The complexity of current systems that translate natural language to SQL queries is very high. Read up on how our fully transformer-based model enabled us to make things much simpler! I’m deeply indebted to my coworkers at @element_ai, namely Raymond Li, @DBahdanau, Harm de Vries, and @chrisjpal. It was truly a team effort! We published the code, too: <LINK>. @ipvkyte I won’t deny that I have thought about doing that 😅 @ipvkyte I very much agree, I’m working on a port of @PyTorch to Haskell also because I think the language is a great fit for neural program synthesis. @ipvkyte @PyTorch Thanks! Indeed, we’ve been struggling with the GC a bit, and there’s certainly room for improvement, but the library works already quite well. @mandubian Sweet, let me know what you think 😃 @lzamparo Thanks Lee! @lzamparo It is very gratifying, all the hard work is starting to pay off 😃",https://arxiv.org/abs/2010.11119,"Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. We begin by building DuoRAT, a re-implementation of the state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware or vanilla transformers as the building blocks. We perform several ablation experiments using DuoRAT as the baseline model. Our experiments confirm the usefulness of some techniques and point out the redundancy of others, including structural SQL features and features that link the question with the schema. ",DuoRAT: Towards Simpler Text-to-SQL Models,8,"['Our new text-to-SQL paper is now on the ArXiv!\n<LINK>\n\nThe complexity of current systems that translate natural language to SQL queries is very high.\n\nRead up on how our fully transformer-based model enabled us to make things much simpler!', 'I’m deeply indebted to my coworkers at @element_ai, namely Raymond Li, @DBahdanau, Harm de Vries, and @chrisjpal. It was truly a team effort!\n\nWe published the code, too: https://t.co/wPul9GeLnL.', '@ipvkyte I won’t deny that I have thought about doing that 😅', '@ipvkyte I very much agree, I’m working on a port of @PyTorch to Haskell also because I think the language is a great fit for neural program synthesis.', '@ipvkyte @PyTorch Thanks! Indeed, we’ve been struggling with the GC a bit, and there’s certainly room for improvement, but the library works already quite well.', '@mandubian Sweet, let me know what you think 😃', '@lzamparo Thanks Lee!', '@lzamparo It is very gratifying, all the hard work is starting to pay off 😃']",20,10,934