A Discriminative Approach to Grounded Spoken Language Understanding in Interactive Robotics
Authors: Emanuele Bastianelli, Danilo Croce, Andrea Vanzo, Roberto Basili, Daniele Nardi
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results achieve up to a 40% of relative error reduction. |
| Researcher Affiliation | Academia | 1DICII, 2DII, University of Rome Tor Vergata, Rome, Italy 3DIAG, Sapienza University of Rome, Rome, Italy bastianelli@ing.uniroma2.it, {croce,basili}@info.uniroma2.it, {vanzo,nardi}@dis.uniroma1.it |
| Pseudocode | No | The paper describes the steps of the proposed approach in natural language text but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'The SVMhmm algorithm has been implemented within the Ke LP framework [Filice et al., 2015].' This refers to a third-party framework and does not indicate that the authors' own implementation for the described methodology is open source. |
| Open Datasets | Yes | The evaluation is carried out using the Human-Robot Interaction Corpus (Hu RIC, [Bastianelli et al., 2014b]) a collection of utterances annotated with semantic predicates and paired with (possibly multiple) audio files. |
| Dataset Splits | Yes | Measures have been carried out on four tasks, all according to a 5-fold evaluation schema. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like the 'Ke LP framework' and 'Core NLP parser' but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | By applying the settings min-count=50, window=5, iter=10 and negative=10 onto the Uk Wa C corpus we derived 250 dimensional word vectors for more than 110, 000 words. The SVMhmm algorithm has been implemented within the Ke LP framework [Filice et al., 2015]. |