LS-Tree: Model Interpretation When the Data Are Linguistic
Authors: Jianbo Chen, Michael Jordan3454-3461
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out experiments to analyze the performance of four different models: Bag of Words (Bo W), Word-based Convolutional Neural Network (CNN) (Kim 2014), bidirectional Long Short-Term Memory network (LSTM) (Hochreiter and Schmidhuber 1997), and Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al. 2018), across three sentiment data sets of different sizes: Stanford Sentiment Treebank (SST) (Socher et al. 2013), IMDB Movie reviews (Maas et al. 2011) and Yelp reviews Polarity (Zhang, Zhao, and Le Cun 2015). |
| Researcher Affiliation | Academia | Jianbo Chen University of California, Berkeley jianbochen@berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu |
| Pseudocode | Yes | Algorithm 1 LS-Tree Interaction Detection and Algorithm 2 Recursion are present. |
| Open Source Code | Yes | The code for replicating the experiments is available online at https://github.com/Jianbo-Lab/LS-Tree. |
| Open Datasets | Yes | Stanford Sentiment Treebank (SST) (Socher et al. 2013), IMDB Movie reviews (Maas et al. 2011) and Yelp reviews Polarity (Zhang, Zhao, and Le Cun 2015). |
| Dataset Splits | No | Table 1 lists 'Train Size' and 'Test Size' for the datasets, but no explicit validation set split is provided for the experiments conducted in the paper. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used to run the experiments (e.g., GPU or CPU models). |
| Software Dependencies | No | The paper mentions software components like 'GloVe word embedding' and 'Stanford constituency parser' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The CNN is composed of three 100-dimensional convolutional 1D layers with 3, 4 and 5 kernels respectively, concatenated and fed into a max-pooling layer followed by a hidden dense layer. The LSTM uses a bidirectional LSTM layer with 128 units for each direction. |