Event Representations With Tensor-Based Compositions
Authors: Noah Weber, Niranjan Balasubramanian, Nathanael Chambers
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed tensor models on a variety of event related tasks, comparing against a compositional neural network model, a simple multiplicative model, and an averaging baseline. We use the New York Times Gigaword Corpus for training data. |
| Researcher Affiliation | Academia | Noah Weber Stony Brook University Stony Brook, New York, USA nwweber@cs.stonybrook.edu Niranjan Balasubramanian Stony Brook University Stony Brook, New York, USA niranjan@cs.stonybrook.edu Nathanael Chambers United States Naval Academy Annapolis, Maryland, USA nchamber@usna.edu |
| Pseudocode | No | The paper describes algorithms in prose and provides mathematical equations, but it does not contain a dedicated 'Pseudocode' or 'Algorithm' block with structured, code-like formatting. |
| Open Source Code | Yes | We make all code and data publicly available.3 github.com/stonybrooknlp/event-tensors |
| Open Datasets | Yes | We use the New York Times Gigaword Corpus for training data. The transitive sentence similarity dataset (Kartsaklis and Sadrzadeh 2014a) contains 108 pairs of transitive sentences. |
| Dataset Splits | No | We hold out 4000 articles from the corpus to construct dev sets for hyperparameter tuning, and 6000 articles for test purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like Ollie and GloVe, and optimizers like Adagrad, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We initialize the word embedding layer with 100 dimensional pretrained Glo Ve vectors (Pennington, Socher, and Manning 2014)... Training was done using Adagrad (Duchi, Hazan, and Singer 2011) with a learning rate of 0.01 and a minibatch size of 128. |