Hierarchical Poset Decoding for Compositional Generalization in Language
Authors: Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed decoder on Compositional Freebase Questions (CFQ), a large and realistic natural language question answering dataset that is specifically designed to measure compositional generalization. Results show that it outperforms current decoders. (Abstract) |
| Researcher Affiliation | Collaboration | 1Key Laboratory of Computational Linguistics School of EECS, Peking University, Beijing, China; 2Microsoft Research Asia, Beijing, China |
| Pseudocode | Yes | Algorithm 1 generate_poset (the decoding process at inference time) |
| Open Source Code | No | The paper references external tools with links (GIZA++2, Match-Zoo3) but does not provide a link or explicit statement for the open-source code of their proposed method. |
| Open Datasets | Yes | Dataset We conduct experiments on three different splits MCD1/MCD2/MCD3 of CFQ dataset (Keysers et al., 2020). |
| Dataset Splits | Yes | Each split contains 95k/12k/12k instances for training/development/test. (Section 6.1) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud computing specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software such as GIZA++2, Match-Zoo3, and PyTorch, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The dimensions of the hidden state and word embedding are set to 512, 300 respectively. The training process lasts 50 epochs with batch size 64/256 for sketch prediction and traversal path prediction, respectively. We use the Adam optimizer with default settings (in PYTORCH) and a dropout layer with the rate of 0.5. (Section 6.1) |