Predicting Concrete and Abstract Entities in Modern Poetry
Authors: Fiammetta Caccavale, Anders Søgaard858-864
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper considers the problem of teaching a neural language model to select poetic entities, based on local context windows. We do so by fine-tuning and evaluating language models on the poetry of American modernists, both on seen and unseen poets, and across a range of experimental designs. |
| Researcher Affiliation | Academia | Fiammetta Caccavale, Anders Søgaard University of Copenhagen Universitetsparken 1 DK-2100 Copenhagen |
| Pseudocode | No | The paper describes the model architecture and training details in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We use a publicly available implementation for our baseline,2 and modify the same code for our system, which we make publicly available here.3 |
| Open Datasets | Yes | Our dataset was collected using the public API Poetry DB (Poetry DB ). The Wikipedia corpus dump used in this study is publicly available.4 https://corpus.byu.edu/wiki/. Accessed [10-02-2018]. |
| Dataset Splits | Yes | The dataset crawled from Poetry DB has been divided into 80% training, 10% development, and 10% test set. |
| Hardware Specification | No | The paper mentions the model size (17.3M parameters) and type (bidirectional LSTMs) but does not specify any hardware details like GPU models, CPU types, or memory used for experiments. |
| Software Dependencies | No | The paper mentions using a bidirectional LSTM, word2vec, and RMSprop, but it does not provide specific version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | Our architecture is the following: the first layer is an embedding layer... The bidirectional LSTM layer has 200 neurons... Training details: The back-propagation algorithm... is run for 10 iterations.The training batch size is set to 32, and the pre-training learning rate used is 0.001... The training batch size is set to 32, the number of iterations to 10, and the learning rate used is 0.0005. |