Update README.md

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]().  
+
+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.  
+
+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.
+
 # Create Environment
-Create the environment with python and activate it:  
-    `python -m venv venv`  
-    `venv\Scripts\activate`  
-or with Anaconda:  
-    `conda create --name venv`  
-    `conda activate venv`  
+Create a new environment:  
+
+    conda create --name venv  
+
+And activate it:  
+
+    conda activate venv
 
 
 # Install Required Lib
 You can install them from `requirements.txt` file with:  
-    `python -m pip install -r requirements.txt`  
-or manually:  
-    - pip install pandas  
-    - pip install sklearn  
-    - pip install torch  
-    - pip install simpletransformers  
-    - pip install xlsxwriter  
-    - pip install nltk  
-    - pip install spacy  
+
+    pip install -r requirements.txt  
+
 Then download spacy:  
-    - python -m spacy download en_core_web_sm  
-    - python -m spacy download it_core_news_sm  
+
+    python -m spacy download en_core_web_sm  
 
 **NOTE**: for torch, you need python version <= 3.9.x    
-**NOTE**: you need specific version of torch based on cuda version (our actual version is cuda11):    
-    `python -m pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html`  
+**NOTE**: you need specific version of torch based on cuda version (our current version is cuda11):    
 
-### if any error try:
-    pip install simpletransformers --use-feature=2020-resolver  
-### or
-    pip install pandas, sklearn, torch, xlsxwriter, simpletransformers --use-feature=2020-resolver  
+    python -m pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
 
-# Other requirements
+
+## Other requirements
 - HuRIC 2.1 dataset. You can download it manually and put it in `data` folder or use:  
+
     `git clone https://github.com/crux82/huric.git data`  
+
 - You should use `nohup` or `nodemon` to start your training, as you don't want to wait for it to finish :)  
 
 # How to train!
-In **bold** you will find the name of the model as reported in the paper and as code the command to execute in order to train it.  
+This repository provides the code to fine tune 2 models: **GrUT**, that takes a command and the environment description in natural language as input to provide the interpretation, and **BART**, that takes only the command as input to provide the interpretation.
+In **bold** you will find the name of the model, as reported in the paper, a brief description and the code to execute the command in order to train it.  
+
 - **BART_base**: BART base, as released by huggingface  
+
     `nohup python -u main.py train -mn bart -mv base -t SRL -bs 32 -uc True -ep 50 -nf 10 -tt SRL > logs/testing_verbose_with_prints_bart_base.out &`  
 
 - **GRUT**: BART with existential and spatial map description  
-    `nohup python -u main.py train -mn bart -mv base -t SRL -bs 32 -uc True -ep 50 -nf 10 -tt SRL -gr half -map lmd > logs/testing_verbose_with_prints_grut.out &`  
 
-- **GRUT_LU**: BART with existential and spatial map description and Frames evoked by every single noun  
-    `nohup python -u main.py train -mn bart -mv base -t SRL -bs 32 -uc True -ep 50 -nf 10 -tt SRL -gr half -map lmd -alut True > logs/testing_verbose_with_prints_grut_lu.out &`  
+    `nohup python -u main.py train -mn bart -mv base -t SRL -bs 32 -uc True -ep 50 -nf 10 -tt SRL -gr half -map lmd > logs/testing_verbose_with_prints_grut.out &`  
+
 
 ### Options
+Here you can find a list of the options used to train the models.
+
     -h : help.
-    -nf : define numbers of fold in kfold. Only if evaluation mode is True.
+    -am : define whether to add map description or not, default False.
+    -bs : define batch size, 4 by default.
+    -ep : define numbers of epochs for training, 1 by default.
+    -es : define if using early stopping considering epoch during training or not, the default is True.
+    -mn : define model name: bart (default), t5, mt5.
+    -mv : define model size: small, base (default), large, ecc.
+    -nf : define numbers of fold in kfold.
     -uc : define if use GPU True/False.
-    -ep : define numbers of epochs for training.
     -t  : define task type: FP(Frame Prediction), BD(Boundary Detection), AC(Argument Classification), SRL (default).
     -tt : define type of target manipulation: frame, frame+pos, frame+token, frame+sentence, SRL(default).
-    -es : define if using early stopping considering epoch during training or not.
-    -mn : define model name: bart (default), t5, mt5.
-    -mv : define model size: small, base, large, ecc.
-    -am : define whether to add map description or not, default False.
+
 
 # Results
-Results will be written inside `./model/<model_name>`.   
-For example: if you want to train *GrUT* (name as in the paper, which means it will be trained with the lexicalized map description in input, will use the W2V retrieval method and it will perform Semantic Role Labling), <model_name> will be `bart_en_stm_lmd_halfgrounding_W2V_allLexicalReferences_<TIMESTAMP>` based on the options described here. There you will find, among others, 3 files:  
+Results will be written inside `./model/<model_name>` folder.   
+For example: if you want to train **GrUT**, <model_name> will be `bart_en_stm_lmd_halfgrounding_W2V_allLexicalReferences_<TIMESTAMP>` based on the options described here. There you will find, among others, 3 files:  
 - `results_unified.xlsx` containing test set sentences, with predictions and gold standard (truth)  
 - `frames_CM_unified.txt` containing confusion matrix for frames only (Frame Prediction task), merged for all X folds  
 - `frame_elements_CM_unified.txt` containing confusion matrix for frame elements (arguments and types for AIC task), merged for all X folds