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README.md

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 # Introduction
-This repository contains the code for the paper **GrUT (Grounded language Understanding via Transformers): Embedding Contextual Information in seq2seq models for Grounded Semantic Role Labeling** published in the **AIxIA 2022** conference by *Claudiu Daniel Hromei* (Tor Vergata, University of Rome; Università Campus Bio-Medico di Roma), *Lorenzo Cristofori* (Tor Vergata, University of Rome), *Danilo Croce* (Tor Vergata, University of Rome) and *Roberto Basili* (Tor Vergata, University of Rome). The paper can be found [here]().  
+This repository contains the code for two papers:
+- **GrUT 1.0 (Grounded language Understanding via Transformers): Embedding Contextual Information in seq2seq models for Grounded Semantic Role Labeling** published in the **AIxIA 2022** conference by *Claudiu Daniel Hromei* (Tor Vergata, University of Rome; Università Campus Bio-Medico di Roma), *Lorenzo Cristofori* (Tor Vergata, University of Rome), *Danilo Croce* (Tor Vergata, University of Rome) and *Roberto Basili* (Tor Vergata, University of Rome). The paper can be found [here]().  
+- **GrUT 2.0 (Grounded language Understanding via Transformers): Grounding end-to-end Architectures for Semantic Role Labeling in Human Robot Interaction** published in the **NL4AI** workshop at AIxIA 2022 conference by *Claudiu Daniel Hromei* (Tor Vergata, University of Rome; Università Campus Bio-Medico di Roma), *Danilo Croce* (Tor Vergata, University of Rome) and *Roberto Basili* (Tor Vergata, University of Rome). The paper can be found [here]().  
 
 GrUT is a neural approach for the interpretation of robotic spoken commands that is consistent with (i) the world (with all the entities therein), (ii) the robotic platform (with all its inner representations and capabilities), and (iii) the linguistic information derived from the user’s utterance. It is a sequence-to-sequence method that performs Grounded Semantic Role Labeling in an end-to-end manner, thus avoiding the traditional cascade of interpretation tasks to be solved and effectively linking arguments on the basis of the status and properties of the real-world.  
 
+We released here GrUT 2.0, that is an extension of the original work where the model is expected to produce indexed interpretations in the robot Knowledge Base through the identifier of the real existing entities. In particular, the interpretation is consistent both with the command *and* the map of the surrounding environment.  
+In the 1.0 version, all the entities, whose name exactly match at least one word in the command, are retrieved and fed in the input. The Exact Match is not suitable to retrieve objects like *volume* or *sofa* if the command contains *book* or *couch*. In the extended work, we propose 2 additional *LexicalSimilarity* functions to retrieve entities:
+- *Levenshtein Similarity*, based on the Levenshtein Distance
+- *Neural Semantic Similarity*, based on W2V embeddings
+
 This code runs the GrUT experiment over a public dataset ([HuRIC 2.1](https://github.com/crux82/huric)) for the Semantic Role Labeling (SRL) task and compares its performances with a simple ready to use model, namely BART, finetuned on the same dataset.  
 
 As a result, we show an error reduction of 26% for the Frame Prediction (FP) task and 23% for the Argument Identification and Classification (AIC) task. You can find more details about the tasks in the paper.