Toward Controlled Generation of Text
Authors: Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative experiments using trained classifiers as evaluators validate the accuracy of sentence and attribute generation. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2Petuum, Inc.. Correspondence to: Zhiting Hu <zhitingh@cs.cmu.edu>. |
| Pseudocode | Yes | Algorithm 1 Controlled Generation of Text |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use a large IMDB text corpus (Diao et al., 2014) for training the generative models. Stanford Sentiment Treebank-2 (SST-full) (Socher et al., 2013) consists of 6920/872/1821 movie review sentences with binary sentiment annotations in the train/dev/test sets, respectively. Lexicon from (Wilson et al., 2005). We compile from the Time Bank (timeml.org) dataset. |
| Dataset Splits | Yes | Stanford Sentiment Treebank-2 (SST-full) (Socher et al., 2013) consists of 6920/872/1821 movie review sentences with binary sentiment annotations in the train/dev/test sets, respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names like PyTorch or TensorFlow with their versions). |
| Experiment Setup | Yes | The generator and encoder are set as single-layer LSTM RNNs with input/hidden dimension of 300 and max sample length of 15. Discriminators are set as Conv Nets. ... we use a KL term weight linearly annealing from 0 to 1 during training. Balancing parameters are set to λc = λz = λu = 0.1, and β is selected on the dev sets. |