Co-Acquisition of Syntax and Semantics — An Investigation in Spatial Language
Authors: Michael Spranger, Luc Steels
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of codevelopment of syntax and semantics. |
| Researcher Affiliation | Collaboration | Michael Spranger1 and Luc Steels2 1) Sony Computer Science Laboratories Inc., Tokyo, Japan, michael.spranger@gmail.com 2) ICREA, Institut de Biologia Evolutiva, Barcelona, Spain, steels@arti.vub.ac.be |
| Pseudocode | No | The paper includes figures (Figure 2, Figure 3) showing examples of "IRL-program" structures, which are code-like, but these are representations of meaning, not pseudocode for the algorithms or learning steps, and are not labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code available or include links to code repositories. |
| Open Datasets | No | The paper states, "For the purpose of evaluation we recorded more than 1000 spatial scenes with different numbers of objects...", indicating a custom dataset was created. However, it does not provide concrete access information (link, DOI, repository, or formal citation) for this dataset to be publicly available. |
| Dataset Splits | No | The paper mentions running "25 experimental runs" and "100 experiments" but does not provide specific details on how the "1000 spatial scenes" were split into training, validation, or test sets (e.g., percentages, sample counts, or methodology for creating splits). |
| Hardware Specification | No | The paper mentions using "developmental robots" and a "vision system" but does not provide any specific hardware details such as CPU/GPU models, memory, or other computational resources used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Fluid Construction Grammar" and "Incremental Recruitment Language (IRL)", but it does not specify any software names with version numbers for dependencies required to replicate the experiments. |
| Experiment Setup | No | The paper describes the general learning strategies and the tutor's role in incrementally increasing complexity, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes, number of epochs) or specific optimizer settings used in the simulations. |