**Company: INALCO Job Description Summary: Inalco is seeking a Junior Professor in “Artificial Intelligence for Rare or Under-Resourced Languages” for a fixed-term 5-year contract leading to tenure as a full professor. The research focuses on digitization and analysis of resources, with emphasis on multilingual data processing. The candidate must have expertise in NLP methods and be able to design and develop research for under-resourced languages. The role also involves teaching undergraduate and postgraduate courses in NLP and Digital Humanities at Master’s and Doctoral levels. Candidates should hold a Ph.D. and have a strong research record. Fluency in French is not required initially but must be acquired by the end of the contract. Candidates will be evaluated based on excellence in research, motivation, and supervisory skills. Interviews may be conducted in French or English. Job Description: Inalco, (l’Institut national des langues et civilisations orientales, the National Institute of Oriental Languages and Civilizations) invites applications for a Junior Professorship in “Artificial Intelligence for Rare or Under-Resourced Languages.” This is a fixed-term contract (5 years) with a reduced teaching load, after evaluation leading ultimately to tenure as a full professor. Applications must be submitted no later than September 2, 2024, 4 p.m. (Paris time, UTC+02:00) with the appointment to begin no later than December 31, 2024. Online link: https://www.inalco.fr/institut/concours-recrutement/chaire-de-professeur-junior-intelligence-artificielle-pour-les RESEARCH Inalco is a specialized top-level public institution of higher education and research, recognized for its contribution to the study of the world’s languages, cultures and societies across a range of humanities and social sciences disciplines such as linguistics, sociology, economics, geography, history, international relations, political science, as well as literary and artistic studies. The Institute’s research and teaching activities cover a vast range of languages (over 100) and cultural areas, giving it a singular and internationally recognized academic reputation. The digitization of resources (languages, texts, documentation, cultural objects) and their analysis represent a major challenge for Inalco, both in terms of research implementation (digital humanities, access to primary data, open-source intelligence, digital fieldwork) and the promotion of languages and cultures (access to the information society for speakers of rare and under-resourced languages). Based within Inalco itself, ERTIM (Texts, Computer Science, Multilingualism Research Team, https://ertim.inalco.fr) is a research team specializing in Natural Language Processing (NLP), with a focus on multilingual data processing, multilingual NLP methodologies and language technology applications. The team uses and creates methods and tools for the analysis and processing of languages in both written and spoken datasets. Its collaborative activities focus on Digital Humanities and the description of specific languages (such as Chinese, Arabic, Vietnamese, Bambara, Burmese, Quechua and Hindi), as well as IT applications (text mining, text classification, text annotation, information extraction, etc.). ERTIM is looking for a highly experienced candidate, specialized in current NLP methods (Deep Learning, Large Language Models, Speech Processing), capable of designing and developing research for the analysis and processing of under-resourced languages. The successful candidate will be able to demonstrate how their theoretical and applied research experience will enable them to address complex issues in innovative ways, both in terms of linguistic modeling (models of under-resourced or insufficiently resourced languages) and discursive modeling (processing code-switching, creolization, language variation, language contact, etc.). The Junior Chair professorship has a prospect of tenure. In this role, the candidate must be able to conceptualize and supervise the necessary pre- and post-language modeling tasks (eg. speech processing and recognition, OCR, automatic translation. Through their expertise and associated research projects, the person recruited will also participate in discussions within the institute on the use of Artificial Intelligence in teaching and research for languages and for areal studies based on oriental language sources. More specifically, they will be involved in the development of a range of teaching tools for language teachers wishing to integrate the use of AI, and methodological tools for students and researchers unable to undertake fieldwork in person due to physical restrictions. Affiliated Research Team: ERTIM Location: Maison de la recherche de l’Inalco, 2 rue de Lille, 75007, Paris, France Research Unit Director: Damien Nouvel Telephone: +33 1 81 70 10 37 E-mail: Website: https://ertim.inalco.fr TEACHING The person recruited will teach undergraduate and postgraduate (1st and 2nd cycle) courses in Inalco’s NLP training program and, more widely, as part of general training in Digital Humanities and language data processing methodology, at Master’s and Doctoral levels. Students of these courses are distinctive, in that they often come from humanities and social science backgrounds, and are speakers (or learners) of languages from the Inalco sphere of reference (Asia, Africa, the Americas, Oceania, Eastern Europe). Courses will be chosen in consultation with the teaching team and according to the skills of the person recruited. They may be generic (algorithms, programming, etc.), methodological (processing and contextualization of data from digital fieldwork) or specific to the candidate’s areas of expertise. The creation of new courses may also be envisaged, in line with the teaching and learning objectives of existing programs. The Inalco NLP Master’s is co-accredited with Sorbonne Nouvelle University and Paris Nanterre University. The degree comprises separate research and professional streams, leading to careers in data science, NLP development, computational linguistics, etc. The teaching load will be 64 hours in the first year, 96 hours in the second and third years, and 128 hours in the fourth and fifth years (considered “equivalent TD”, French academic standard tutorials/seminars). At the end of the contractual period, if hired as a full professor, the statutory teaching load will be 192 hours.
Company:INALCO
Qualifications: Candidates should hold a PhD and have a well-established research record showing research activity in areas relevant to the job description (see below), such as publications in leading international journals, academic involvement in research projects, etc. The successful candidate will be able to demonstrate proficiency in one of the languages of their area of specialization, as well as the ability to teach in English. Fluency in French is not required at the time of recruitment, but the candidate is expected to have acquired a sufficient level of competence by the end of the contract, i.e. at the time of tenure. Candidates will be evaluated by a recruitment committee comprising internal and external experts. Only those candidates shortlisted by the committee will be invited to an interview based on a review of their applications. Evaluation criteria will focus on the candidate’s excellence, motivation and supervisory skills; the quality and originality of their proposed research and teaching activities; their capacity to integrate their proposals within the research team and to coordinate the Chair’s program activities, as well as their ability to establish collaborative networks on an international scale. Interviews may be conducted in French or English.
Educational level:Ph. D.
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
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Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.
