text:   Specialized Architectures",6,"['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] https://t.co/VfURdjki4C', '(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.']",20,07,1156