Learning to Recombine and Resample Data For Compositional Generalization
Authors: Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas
ICLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate R&R on two tests of compositional generalization: the SCAN instruction following task (Lake & Baroni, 2018) and a few-shot morphology learning task derived from the SIGMORPHON 2018 dataset (Kirov et al., 2018; Cotterell et al., 2018). Our experiments are designed to explore the effectiveness of learned data recombination procedures in controlled and natural settings. |
| Researcher Affiliation | Academia | Ekin Akyürek MIT CSAIL EMAIL Afra Feyza Akyürek Boston University EMAIL Jacob Andreas MIT CSAIL EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | Code for all experiments in this paper is available at https://github.com/ekinakyurek/compgen. |
| Open Datasets | Yes | We evaluate R&R on two tests of compositional generalization: the SCAN instruction following task (Lake & Baroni, 2018) and a few-shot morphology learning task derived from the SIGMORPHON 2018 dataset (Kirov et al., 2018; Cotterell et al., 2018). |
| Dataset Splits | Yes | We construct splits of the data featuring a training set of 1000 examples and three test sets of 100 examples. ... we construct five different splits per language and use the Spanish past-tense data as a development set. |
| Hardware Specification | Yes | We use a single 32GB NVIDIA V100 Volta GPU for each experiment. |
| Software Dependencies | Yes | We implemented our experiments in Knet (Yuret, 2016) using Julia (Bezanson et al., 2017). |
| Experiment Setup | Yes | Morphology: The hidden and embedding sizes are 1024. No dropout is applied. ... SCAN: We choose the hidden size as 512, and embedding size as 64. We apply 0.5 dropout to the input. |