KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
Authors: Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He13924-13932
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over multiple datasets show KEML outperforms state-of-the-art methods.In this section, we conduct extensive experiments to evaluate KEML over multiple benchmark datasets, and compare it with state-of-the-arts to make the convincing conclusion. |
| Researcher Affiliation | Collaboration | Chengyu Wang,1, 2 Minghui Qiu,2 Jun Huang,2 Xiaofeng He3 1 Zhejiang Lab 2 Alibaba Group 3 School of Computer Science and Technology, East China Normal University |
| Pseudocode | Yes | Algorithm 1 Meta-Learning Algorithm for LRC |
| Open Source Code | No | The paper states: 'We use the uncased, base version of BERT. See https://github. com/google-research/bert.' but does not provide a link or statement for the open-sourcing of KEML's code. |
| Open Datasets | Yes | We use the five public benchmark datasets in English for multi-way classification of lexical relations to evaluate KEML, namely, K&H+N (Necsulescu et al. 2015), BLESS (Baroni and Lenci 2011), ROOT09 (Santus et al. 2016b), EVALution (Santus et al. 2015) and Cog ALex-V Subtask 2 (Santus et al. 2016a). |
| Dataset Splits | Yes | K&H+N, BLESS, ROOT09 and EVALution are partitioned into training, validation and testing sets, following the exact same settings as in Shwartz and Dagan (2016). The Cog ALex-V dataset has training and testing sets only, with no validation sets provided (Santus et al. 2016a). Hence, we randomly sample 80% of the training set to train the model, and use the rest for tuning. |
| Hardware Specification | Yes | The algorithms are implemented with Tensor Flow and trained with NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper states 'The algorithms are implemented with Tensor Flow' but does not provide specific version numbers for TensorFlow or other software dependencies. |
| Experiment Setup | Yes | The default hyper-parameter settings of KEML are as follows: N = |R| 1, γ = 1 and α = ϵ = 10 3. We use tanh as the activation function, and Adam as the optimizer to train the neural network. All the model parameters are l2regularized, with the hyper-parameter λ = 10 3. The batch size is set as 256. The dimension of hidden layers is set as the same of d (768 for the base BERT model). |