Modeling Compositionality with Multiplicative Recurrent Neural Networks
Authors: Ozan Irsoy and Claire Cardie
ICLR 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that multiplicative RNNs provide comparable or better performance than conventional additive recurrent nets and matrix-space models in terms of fine-grained sentiment detection accuracy. Furthermore, although the absence of parse tree information puts an additional learning burden on multiplicative RNNs, we find that they can reach comparable performance to the recursive neural network variants that require parse tree annotations for each sentence. |
| Researcher Affiliation | Academia | Ozan Irsoy & Claire Cardie Department of Computer Science Cornell University Ithaca, NY {oirsoy,cardie}@cs.cornell.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | Yes | For experimental evaluation of the models, we use the manually annotated MPQA corpus (Wiebe et al., 2005) ... Additionally, we use the recently published Stanford Sentiment Treebank (SST) (Socher et al., 2013)... For pretrained word vectors, we use publicly available 300 dimensional word vectors by Mikolov et al. (2013b), trained on part of Google News dataset (∼100B words). |
| Dataset Splits | Yes | We use the training-validation-test set partitions provided by the authors to apply 10-fold CV and report average performance over ten folds." (MPQA) and "We use the single training-validation-test set partition provided by the authors." (SST) |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | For experiments on the MPQA corpus, we employ an ordinal regression setting. For experiments on SST, we employ a simple multiclass classification setting...output nonlinearity g is the softmax function, and the output y is a vector valued response with the class probabilities. Ordinal regression setting is as described in Section 4.2... We experiment with both randomly initialized word vectors (RAND) and pretrained word vector representations (VEC). For pretrained word vectors, we use publicly available 300 dimensional word vectors by Mikolov et al. (2013b)...To test the effect of different nonlinearities, we experiment with the identity, rectifier and tanh functions with m RNNs. |