UE228 - Algorithmes pour le traitement automatique de la parole et du langage

Type d'UE
Méthodologie
Disciplines
Méthodes et techniques des sciences sociales
Page web
https://github.com/edupoux/MVA_2020_SL 
Langues
anglais
Mots-clés
Intelligence artificielle Linguistique
Aires culturelles
-

Speech and natural language processing is a subfield of artificial intelligence used in an increasing number of technological applications but also scientific applications in in digital humanities, medical science and behavioral science. Yet, while some aspects are on par with human performances, others are lagging behind. This course will present the full stack of speech and language technology, from automatic speech recognition to parsing and semantic processing. The course will present, at each level, the key principles, algorithms and mathematical principles behind the state of the art, and confront them with what is know about human speech and language processing. Students will acquire detailed knowledge of the scientific issues and computational techniques in automatic speech and language processing and will have hands on experience in implementing and evaluating the important algorithms.

Program

  • Introduction 
  • ASR1: Features and Acoustic Models 
  • ASR2: Language Models  + presentation TD#1
  • NLP1: Language processing in the wild 
  • NLP2: Formal languages 
  • NLP3: Parsing + presentation TD#2
  • Automatic Translation 
  • Chatbots and open issues

Validation

The validation is continuous: there is no final exam, but a combination of quizzes during the lessons (20%) and two practical assignments (TDs), (40% each). ATTENTION: since there is no exam, there is no possibility of "rattrapage" (ie, of compensating a bad mark by taking another exam). So, if the overall grade obtained in this course is less than 10/20, this course will not be considered validated by the MVA Master.

Practical assignments (TD)

The practical assignments are given on the courses #3 and #6. There will be one assignment for the speech part and one for the NLP part. For each assignment, students are provided with the necessary data and Python code, either as a list of requirements to install or in the form of a disk image (.ova) to be mounted and booted from a virtual machine. They will hand in their source code and a max two page report, detailing their work, the difficulties encountered and the results. Students will have a max of 2 weeks to complete the assignment; assignment will be graded from 0 to 20, with a -1 point removed from the score for each day of being late. Each assignment will count for 40% of the final grade. We may organise special Q&A sessions regarding these assignments from 11am to 12am upon request.

Quizzes

During the courses, we will use on-line quizzes (on the smartphone/computer) to probe comprehension and trigger discussion. The quizzes will be used (1) to check that you attend the course, (2) that you have read the supporting documents and are following what is being presented. Each quiz will be graded as follows: 0 (no response), 1 (wrong response), 2 (good response). The scores will be averaged and converted from on a 0 to 20 scale. If there are N quizzes, we will use the N-1 best scores for averaging. The overall score will count for 20% of the final grade.

  • Sciences cognitives – M2/S3
    Suivi et validation – semestriel hebdomadaire = 6 ECTS
    MCC – autre (travaux pratiques), contrôle continu
  • Emmanuel Dupoux [référent·e]   directeur d'études, EHESS / Laboratoire de sciences cognitives et psycholinguistiques (LSCP)
Contacts additionnels
-
Informations pratiques

le cours a lieu à l'ENS et/ou en ligne. Ce cours est mutualisé avec le master MVA de l'ENS Cachan ; peuvent le valider également les étudiants du master MASH (Dauphine) et du master data science (Polytechnique).

Direction de travaux des étudiants
-
Réception des candidats
-
Pré-requis

Basic linear algebra, calculus, probability theory.

  • Autre lieu Paris
    ENS, 29 rue d’Ulm 75005 Paris
    1er semestre / hebdomadaire, lundi 09:00-12:00
    du 4 janvier 2021 au 15 mars 2021


Intervenant·e·s


  • Emmanuel Dupoux [référent·e]   directeur d'études, EHESS / Laboratoire de sciences cognitives et psycholinguistiques (LSCP)

Planning


  • Autre lieu Paris
    ENS, 29 rue d’Ulm 75005 Paris
    1er semestre / hebdomadaire, lundi 09:00-12:00
    du 4 janvier 2021 au 15 mars 2021


Description


Type d'UE
Méthodologie
Disciplines
Méthodes et techniques des sciences sociales
Page web
https://github.com/edupoux/MVA_2020_SL 
Langues
anglais
Mots-clés
Intelligence artificielle Linguistique
Aires culturelles
-

Speech and natural language processing is a subfield of artificial intelligence used in an increasing number of technological applications but also scientific applications in in digital humanities, medical science and behavioral science. Yet, while some aspects are on par with human performances, others are lagging behind. This course will present the full stack of speech and language technology, from automatic speech recognition to parsing and semantic processing. The course will present, at each level, the key principles, algorithms and mathematical principles behind the state of the art, and confront them with what is know about human speech and language processing. Students will acquire detailed knowledge of the scientific issues and computational techniques in automatic speech and language processing and will have hands on experience in implementing and evaluating the important algorithms.

Program

  • Introduction 
  • ASR1: Features and Acoustic Models 
  • ASR2: Language Models  + presentation TD#1
  • NLP1: Language processing in the wild 
  • NLP2: Formal languages 
  • NLP3: Parsing + presentation TD#2
  • Automatic Translation 
  • Chatbots and open issues

Validation

The validation is continuous: there is no final exam, but a combination of quizzes during the lessons (20%) and two practical assignments (TDs), (40% each). ATTENTION: since there is no exam, there is no possibility of "rattrapage" (ie, of compensating a bad mark by taking another exam). So, if the overall grade obtained in this course is less than 10/20, this course will not be considered validated by the MVA Master.

Practical assignments (TD)

The practical assignments are given on the courses #3 and #6. There will be one assignment for the speech part and one for the NLP part. For each assignment, students are provided with the necessary data and Python code, either as a list of requirements to install or in the form of a disk image (.ova) to be mounted and booted from a virtual machine. They will hand in their source code and a max two page report, detailing their work, the difficulties encountered and the results. Students will have a max of 2 weeks to complete the assignment; assignment will be graded from 0 to 20, with a -1 point removed from the score for each day of being late. Each assignment will count for 40% of the final grade. We may organise special Q&A sessions regarding these assignments from 11am to 12am upon request.

Quizzes

During the courses, we will use on-line quizzes (on the smartphone/computer) to probe comprehension and trigger discussion. The quizzes will be used (1) to check that you attend the course, (2) that you have read the supporting documents and are following what is being presented. Each quiz will be graded as follows: 0 (no response), 1 (wrong response), 2 (good response). The scores will be averaged and converted from on a 0 to 20 scale. If there are N quizzes, we will use the N-1 best scores for averaging. The overall score will count for 20% of the final grade.


Master


  • Sciences cognitives – M2/S3
    Suivi et validation – semestriel hebdomadaire = 6 ECTS
    MCC – autre (travaux pratiques), contrôle continu

Renseignements


Contacts additionnels
-
Informations pratiques

le cours a lieu à l'ENS et/ou en ligne. Ce cours est mutualisé avec le master MVA de l'ENS Cachan ; peuvent le valider également les étudiants du master MASH (Dauphine) et du master data science (Polytechnique).

Direction de travaux des étudiants
-
Réception des candidats
-
Pré-requis

Basic linear algebra, calculus, probability theory.