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<html>
<head>
<title>Multilingual Natural Language Processing</title>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta charset="utf-8" />
<link href="/css/bootstrap.min.css" rel="stylesheet" />
<link href="/css/custom.css" rel="stylesheet" />
<script src="/js/bootstrap.bundle.min.js"></script>
</head>
<body>
<header>
<div class="background">
<video autoplay="true" muted loop>
<source src="/uploads/video/bg.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
<div class="container">
<div class="logo">
<img src="/uploads/imgs/sapienzanlp.png" alt="Sapienza NLP logo" />
</div>
<h1>Multilingual Natural Language Processing</h1>
<h2>From language modeling to understanding and generation across languages</h2>
</div>
</header>
<main>
<!-- Basic info -->
<section>
<div class="container">
<h2>Basic information</h2>
<div class="row">
<div class="col-12 col-lg-7">
<p>
The <strong>Multilingual Natural Language Processing</strong> course introduces the fundamentals of AI for automatically processing,
understanding, and generating human language. Taught in English, this course introduces Natural Language Processing
<strong>from foundational concepts to modern Large Language Models</strong>. Students begin with <strong>words, tokens, tokenization</strong> (including BPE),
<strong>word embeddings</strong> and <strong>language modeling</strong>, and how to evaluate <strong>NLP systems’ outputs</strong>.
</p>
<br />
<p>
The course progresses to <strong>neural networks for NLP, attention</strong>, and the <strong>Transformer architecture</strong>, followed by <strong>Large Language Models</strong>,
their <strong>training phases, scaling laws, PEFT</strong>, and <strong>hands-on applications</strong> (we will also cover the making of our
<a href="https://minerva-ai.org/" target="_blank">Minerva LLM</a> and its current evolution!).
Advanced topics include <strong>machine translation, generative model evaluation, reinforcement learning for LLMs, Retrieval-Augmented Generation,
advanced architectures</strong> (MoE, MAMBA), <strong>semantics, coreference resolution</strong>, and <strong>narrative understanding</strong>.
</p>
<br />
<p>
It is part of the curricula for the Master’s in <strong>AI and Robotics, Engineering in Computer Science and Artificial Intelligence</strong>, and
<strong>Data Science</strong>.
</p>
</div>
<div class="col-12 col-lg-5">
<div class="card">
<div class="h-badge">
Semester
</div>
<p class="mb-0">Spring 2026</p>
<div class="divider"></div>
<div class="h-badge">
When and where
</div>
<p>
<b>February 27 – May 29, 2026</b>
</p>
<ul>
<li>Wednesday (10:15 – 12:00)</li>
<li>Friday (8:30 – 10:45)</li>
</ul>
<p class="mb-0">
<b>Venue</b>: S. Pietro in Vincoli, via delle Sette Sale, 29 (room/aula 41)
</p>
<div class="divider"></div>
<div class="h-badge">
Classroom
</div>
<p class="mb-0">
All the class material can be found in the dedicated classroom:
<a href="https://classroom.google.com/c/ODQyOTcxMjM1NTQx" target="_blank">
https://classroom.google.com/c/ODQyOTcxMjM1NTQx
</a>
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Basic info -->
<!-- Course Syllabus -->
<section>
<div class="container">
<h2>Course Syllabus</h2>
<div class="row align-items-stretch row-modules">
<!-- Foundations of NLP -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-01.png" /></div>
<h3 class="title">Foundations of NLP: words, tokens and language models</h3>
<ul class="topics">
<li>Introduction to the course</li>
<li>Words & tokens</li>
<li>Tokenization techniques</li>
<li>Language models</li>
<li>Evaluation basics</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">Language modeling</div>
</div>
</div>
</div>
<!-- ML for NLP -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-02.png" /></div>
<h3 class="title">Machine Learning for NLP</h3>
<ul class="topics">
<li>Machine Learning & classification</li>
<li>Logistic regression</li>
<li>Cross-entropy (CE)</li>
<li>Gradient descent</li>
<li>Evaluation: Accuracy, Precision, Recall, F-measure</li>
<li>Train/dev/test sets & statistical significance</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">Logistic regression</div>
</div>
</div>
</div>
<!-- Word Representations & Semantics -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-03.png" /></div>
<h3 class="title">Word Representations & Semantics</h3>
<ul class="topics">
<li>Count-based semantics</li>
<li>Cosine similarity</li>
<li>Word2vec</li>
<li>Embedding properties</li>
<li>Visualization & bias</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">Word embeddings</div>
</div>
</div>
</div>
<!-- Neural Networks & Transformer Encoders -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-04.png" /></div>
<h3 class="title">Neural Networks & Transformer Encoders</h3>
<ul class="topics">
<li>Neural networks & deep learning</li>
<li>The attention mechanism</li>
<li>Transformer (1): The Encoder architecture</li>
<li>From BERT to mmBERT</li>
<li>Sentence embeddings</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">Transformer encoder models</div>
<div class="homework">Homework 1</div>
</div>
</div>
</div>
<!-- Decoders: Large Language Models -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-05.png" /></div>
<h3 class="title">Decoders: Large Language Models</h3>
<ul class="topics">
<li>Transformer (2): The Decoder architecture</li>
<li>Training phases (pretraining, post-training)</li>
<li>Scaling laws</li>
<li>PEFT</li>
<li>Reinforcement learning for LLMs</li>
<li>Retrieval-Augmented Generation</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">LLMs (including Minerva!) and RAG</div>
<div class="homework">Homework 2</div>
</div>
</div>
</div>
<!-- Advanced Topics & Applications -->
<div class="col-12 col-lg-4">
<div class="card card-module">
<div class="preview"><img src="/uploads/imgs/course-06.png" /></div>
<h3 class="title">Advanced Topics & Applications</h3>
<ul class="topics">
<li>Machine Translation</li>
<li>Evaluation of generative models</li>
<li>BabelNet and Word Sense Disambiguation</li>
<li>Semantic Role Labeling and Semantic Parsing</li>
<li>Coreference Resolution</li>
<li>Narrative Understanding</li>
<li>Advanced architectures (MoE, MAMBA)</li>
</ul>
<div class="hands-on">
<strong>Hands-on</strong>
<div class="description">Surprise!</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Course Syllabus -->
<!-- Exams and Assessment -->
<section>
<div class="container">
<h2>Exams and Assessment</h2>
<div class="row align-items-stretch">
<div class="col-12 col-lg-4">
<div class="card h-100">
<div class="h-badge">
Exam sessions
</div>
<ul class="mb-0">
<li><strong>Mon 22/6/26, 13:00–18:00</strong> – 108 Marco Polo</li>
<li><strong>Wed 15/7/26, 8:00–13:00</strong> – 108 Marco Polo</li>
<li><strong>Tue 22/9/26, 8:00–13:00</strong> – 105 Marco Polo</li>
</ul>
<div class="divider"></div>
<div class="h-badge">
Exam
</div>
<p>Upon submission of your homeworks or project (see right box on attending vs. non-attending students), the student will give an oral exam revolving around:</p>
<ul class="mb-0">
<li><strong>Homework presentations</strong> (overall 10 minutes);</li>
<li>Starting from your homeworks, <strong>theory questions</strong> (potentially on the whole course program).</li>
</ul>
</div>
</div>
<div class="col-12 col-lg-8">
<div class="card h-100">
<div class="h-badge">
Attending students
</div>
<p>
Attending students are students who regularly attend the course. They have to complete
<strong>two homeworks</strong>:
</p>
<ul>
<li>One to be delivered during the course;</li>
<li>One to be delivered <strong>10 days before each exam session</strong> and, in any case, <strong>by the September session</strong>.</li>
</ul>
<div class="divider"></div>
<div class="h-badge">
Non-attending students
</div>
<p>
Non-attending students are students submitting both homeworks at any exam session
(including January and February) and/or attending students who failed the homeworks.
They must take the <strong>full exam</strong> (more details in the next slides).
</p>
<p>
Their homeworks must be delivered <strong>10 days before each exam session</strong>
(for instance, if the exam date is December 25th, the homework must be delivered by
December 15th by midnight++; the <strong>++</strong> means we will not be strict with the submission hour).
</p>
<div class="divider"></div>
</div>
</div>
</div>
</div>
</section>
<!-- Exams and Assessment -->
<!-- Teaching Staff -->
<section>
<div class="container">
<h2>Teaching Staff</h2>
<div class="row row-teaching align-items-stretch">
<div class="col-12 col-lg-5">
<div class="card card-navigli">
<div class="h-badge">
Course Instructor
</div>
<h4>Prof. Roberto Navigli</h4>
<div class="divider"></div>
<p><b>Office</b>: room B119, via Ariosto, 25</p>
<p><b>Email</b>: surname chiocciola diag plus uniroma1 plus it (if you are a human being, please replace plus with . and chiocciola with @)</p>
<p><b>Website</b>: <a href="https://www.diag.uniroma1.it/navigli/" target="_blank">https://www.diag.uniroma1.it/navigli/</a></p>
<div class="picture">
<img src="uploads/imgs/people/roberto-navigli.jpg" />
</div>
</div>
</div>
<div class="col-12 col-lg-7">
<div class="card">
<div class="h-badge">
Teaching Assistants
</div>
<div class="row row-assistant">
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/marina-aur.jpg" /></div><div class="name">Marina Aur</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/leonardo-colosi.jpg" /></div><div class="name">Leonardo Colosi</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/alberte-fernandez.jpg" /></div><div class="name">Alberte Fernández</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/bruno-gatti.jpg" /></div><div class="name">Bruno Gatti</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/luca-gioffrè.jpg" /></div><div class="name">Luca Gioffré</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/elena-marafatto.jpg" /></div><div class="name">Elena Marafatto</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/luca-moroni.jpg" /></div><div class="name">Luca Moroni</div></div>
<div class="col-6 col-lg-4"><div class="picture"><img src="/uploads/imgs/people/francesco-ortame.jpg" /></div><div class="name">Francesco Ortame</div></div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaching Staff -->
</main>
<footer>
</footer>
</body>
</html>