Inducing Relational Knowledge from BERT
Authors: Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert7456-7463
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally analyze the performance of our method. Our main question of interest is whether the proposed method allows us to model relations in a better way than is possible with pre-trained word vectors. |
| Researcher Affiliation | Academia | Zied Bouraoui CRIL CNRS & Univ Artois, France zied.bouraoui@cril.fr Jose Camacho-Collados Cardiff University, UK camachocolladosj@cardiff.ac.uk Steven Schockaert Cardiff University, UK schockaerts1@cardiff.ac.uk |
| Pseudocode | No | The paper describes its methodology in prose but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, "We used the BERT implementation available at https://github. com/huggingface/transformers" (Footnote 5). This refers to a third-party library used, not the authors' own source code for their proposed method. |
| Open Datasets | Yes | We consider relations taken from the following three standard benchmark datasets: the Google analogy Test Set (Google)... the Bigger Analogy Test Set (BATS)... the Diff Vec Test Set (DV)... |
| Dataset Splits | No | To this end, for a given relation, we first split the set of available examples in two sets: a training set that contains 90% of words pairs and a test set that contains the remaining 10%. The paper does not mention a separate validation set split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a "BERT implementation" from Hugging Face but does not provide specific version numbers for this or any other software dependencies (e.g., Python version, PyTorch version). |
| Experiment Setup | No | While the paper describes the general experimental process (e.g., filtering templates, fine-tuning BERT, negative sampling), it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. |