Knowledge-driven Natural Language Understanding of English Text and its Applications
Authors: Kinjal Basu, Sarat Chandra Varanasi, Farhad Shakerin, Joaquín Arias, Gopal Gupta12554-12563
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 summarizes the testing statistics and performance metrics of the SQuARE system (benchmarks are tested on an intel i9-9900 CPU with 16G RAM). Table 2 and table 3 compares our results in terms of accuracy with the existing state-of-the-art results for SQuARE and StaCACK system respectively. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, University of Texas at Dallas, USA 2 Artificial Intelligence Research Group, Universidad Rey Juan Carlos, Madrid, Spain |
| Pseudocode | Yes | Algorithm 1 Semantic Knowledge Generation; Algorithm 2 Partial Tree Matching |
| Open Source Code | No | No explicit statement about the release of open-source code for the methodology described in this paper or a direct link to a code repository was found. |
| Open Datasets | Yes | The SQuARE and the StaCACK system have been tested on the bAbI QA (Weston et al. 2015) and the bAbI dialog dataset respectively (Bordes, Boureau, and Weston 2016). |
| Dataset Splits | No | The paper uses the bAbI QA and dialog datasets for evaluation but does not specify custom training, validation, and test splits for its own reasoning-based system, nor does it explicitly state using predefined splits of these datasets for reproduction in a machine learning context. It states: 'Our work is based purely on reasoning and and does not require any manual intervention other than providing (reusable) commonsense knowledge coded in ASP.' |
| Hardware Specification | Yes | Table 1 summarizes the testing statistics and performance metrics of the SQuARE system (benchmarks are tested on an intel i9-9900 CPU with 16G RAM). |
| Software Dependencies | No | The paper mentions using "Stanford Core NLP" and "spaCy" for parsing and "s(CASP) system" for ASP execution, but it does not specify exact version numbers for these software dependencies. |
| Experiment Setup | No | The paper describes the logical components and algorithms of its reasoning-based system but does not provide specific experimental setup details such as hyperparameters, batch sizes, or optimizer settings, as its approach is not based on machine learning training. |