Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems
Authors: [data] Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
ICLR 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present results of various models on these tasks, and evaluate their performance. |
| Researcher Affiliation | Industry | Jesse Dodge , Andreea Gane , Xiang Zhang , Antoine Bordes, Sumit Chopra, Alexander H. Miller, Arthur Szlam & Jason Weston Facebook AI Research 770 Broadway New York, USA {jessedodge,agane,xiangz,abordes,spchopra,ahm,aszlam,jase}@fb.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'We used the code available at: https://github.com/facebook/SCRNNs' (Footnote 9), which is for a baseline LSTM model, not the open-source code for the paper's main methodology or models. |
| Open Datasets | Yes | We provide a dataset covering 75k movie entities and with 3.5M training examples. (...) The datasets are available at: http://fb.ai/babi. |
| Dataset Splits | Yes | We split the questions into training, development and test sets with 96k, 10k and 10k examples, respectively. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running experiments, such as GPU/CPU models or cloud instance types. |
| Software Dependencies | No | The paper states 'All models are implemented in the Torch library (see torch.ch)' but does not provide a specific version number for Torch or any other software dependencies. |
| Experiment Setup | Yes | Hyperparameters of all learning models have been set using grid search on the validation set. The main hyperparameters are embedding dimension d, learning rate λ, number of dictionaries w, number of hops K for Mem NNs and unfolding depth blen for LSTMs. |