Dynamic Compositional Neural Networks over Tree Structure
Authors: Pengfei Liu, Xipeng Qiu, Xuanjing Huang
IJCAI 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our models on two typical tasks: text classification and text semantic matching. The results show that our models are more expressive due to their learning to learn nature, yet without increasing the number of model s parameters. Moreover, we find certain composition operations can be learned implicitly by meta Tree NN, such as the composition of noun phrases and verb phrases. |
| Researcher Affiliation | Academia | Pengfei Liu, Xipeng Qiu , Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The word embeddings for all of the models are initialized with Glo Ve vectors [Pennington et al., 2014]. ... SST The movie reviews with two classes (negative, positive) in the Stanford Sentiment Treebank [Socher et al., 2013b]. MR The movie reviews with two classes [Pang and Lee, 2005]. QC The TREC questions dataset involves six different question types. [Li and Roth, 2002]. SUBJ Subjectivity dataset where the goal is to classify each instance (snippet) as being subjective or objective. [Pang and Lee, 2004] IE Idiom enhanced sentiment classification. [Williams et al., 2015]. ... We choose the dataset of Sentences Involving Compositional Knowledge (SICK), which is proposed by Marelli et al. [2014] aiming at evaluation of compositional distributional semantic models. |
| Dataset Splits | Yes | The dataset consists of 9927 sentence pairs in a 4500/500/4927 train/dev/test split, in which each sentence pairs are pre-defined into three labels: entailment , contradiction and neutral . |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions word embeddings (GloVe) and a constituency parser ([Klein and Manning, 2003]) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The word embeddings for all of the models are initialized with Glo Ve vectors [Pennington et al., 2014]. The other parameters are initialized by randomly sampling from uniform distribution in [ 0.1, 0.1]. The final hyper-parameters are as follows. The initial learning rate is 0.1. The regularization weight of the parameters is 1E 5 and the others are listed as Table 1. |