Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning Scripts as Hidden Markov Models
Authors: John Orr, Prasad Tadepalli, Janardhan Doppa, Xiaoli Fern, Thomas Dietterich
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop an algorithm for structure and parameter learning based on Expectation Maximization and evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partial observation sequences. |
| Researcher Affiliation | Academia | J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, Thomas G. Dietterich EMAIL School of EECS, Oregon State Univserity, Corvallis OR 97331 |
| Pseudocode | Yes | Algorithm 1 procedure LEARN(Model M, Data D, Changes S); Algorithm 2 Forward-Backward algorithm to delete an edge and re-distribute the expected counts. |
| Open Source Code | No | The paper does not provide any specific links or statements indicating that its source code is publicly available. |
| Open Datasets | Yes | The Open Minds Indoor Common Sense (OMICS) corpus was developed by the Honda Research Institute and is based upon the Open Mind Common Sense project (Gupta and Kochenderfer 2004). |
| Dataset Splits | No | The paper mentions a test split ("forty percent of the narratives were withheld for testing") but does not specify a separate validation split for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Wordnet for semantic similarity but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | The paper mentions specific experimental parameters such as "Batch Size r 2 5 10" in Table 1, indicating that `r` is a varying hyperparameter. It also mentions "adding a pseudocount of 1" and a z-test "threshold of 0.01" for constraint learning. |