text: 314,87,1285297480385650689,193887081,Marc van Zee,"New paper! ""Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures"". We assess techniques/architectures's effectiveness in improving compositional generalization based on the SCAN and CFQ datasets. (<LINK>) Highlights [1/5]: (1) We provide the most comprehensive summary so far of architectures and techniques that have been applied to SCAN or CFQ [2/5] <LINK> (2) Pre-training (using T5) helps for compositional generalization, but does not solve it. By combining pre-training with an intermediate representation, we obtain a new SOTA score for CFQ of 42.1% on the MCD-mean split, beating the previous best results of 18.9%. [3/5] (3) For the specialized architectures we evaluated (e.g., Neural Shuffle Exchange Network and CGPS), improvements obtained on one compositional generalization benchmark do not transfer to others. [4/5] (4) Improvements to general-purpose architectures (e.g., LSTM -&gt; Transformer) generally lead to corresponding incremental improvements in compositional settings. [5/5] Cells with a grey background are results we add in our paper; those with a white background are existing results.",https://arxiv.org/abs/2007.08970,"While mainstream machine learning methods are known to have limited ability to compositionally generalize, new architectures and techniques continue to be proposed to address this limitation. We investigate state-of-the-art techniques and architectures in order to assess their effectiveness in improving compositional generalization in semantic parsing tasks based on the SCAN and CFQ datasets. We show that masked language model (MLM) pre-training rivals SCAN-inspired architectures on primitive holdout splits. On a more complex compositional task, we show that pre-training leads to significant improvements in performance vs. comparable non-pre-trained models, whereas architectures proposed to encourage compositional generalization on SCAN or in the area of algorithm learning fail to lead to significant improvements. We establish a new state of the art on the CFQ compositional generalization benchmark using MLM pre-training together with an intermediate representation. ","Compositional Generalization in Semantic Parsing: Pre-training vs.