Ask, and Shall You Receive? Understanding Desire Fulfillment in Natural Language Text
Authors: Snigdha Chaturvedi, Dan Goldwasser, Hal Daume III
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task. Table 4 compares the test set performances using F1 score of the positive (desire fulfilled) class for various models. |
| Researcher Affiliation | Academia | 1University of Maryland, College Park 2Purdue University |
| Pseudocode | Yes | Algorithm 1 Training algorithm for LSNM |
| Open Source Code | No | No explicit statement about releasing the source code for their proposed methodology was found. The paper mentions "BIUTEE: A modular open-source system for recognizing textual entailment" which is a third-party tool they used, not their own code. |
| Open Datasets | Yes | We have used two manually annotated datasets for our experiments: MCTest and Simple Wiki. Both the datasets are available on the first author s webpage. |
| Dataset Splits | Yes | Also, the number of latent states, H, was set to be 2 and 15 for the MCTest and Simple Wiki datasets respectively using cross-validation. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided. The paper mentions tools like "Stanford Core NLP coreference resolution system" and "BIUTEE" (Stern and Dagan 2012; Magnini et al. 2014) but without version details. |
| Experiment Setup | No | No specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings are provided in the main text. |