August 2022 - Corpora

PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processing
by François Portet 01 Jun '23

01 Jun '23

PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processing Starting date: November 1st, 2022 (flexible) Application deadline: September 5th, 2022 Interviews (tentative): September 19th, 2022 Salary: ~2000€ gross/month (social security included) Mission: research oriented (teaching possible but not mandatory) *Keywords:*speech processing, natural language processing, self-supervised learning, knowledge informed learning, Robustness, fairness *CONTEXT* The ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies) will start on November 1st 2022. Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks. *PROJECT OBJECTIVES* Recent SSL models for speech such as HuBERT or wav2vec 2.0 have shown an impressive impact on downstream tasks performance. This is mainly due to their ability to benefit from a large amount of data at the cost of a tremendous carbon footprint rather than improving the efficiency of the learning. Another question related to SSL models is their unpredictable results once applied to realistic scenarios which exhibit their lack of robustness. Furthermore, as for any pre-trained models applied in society, it isimportant to be able to measure the bias of such models since they can augment social unfairness. The goals of this PhD position are threefold: - to design new evaluation metrics for SSL of speech models ; - to develop knowledge-driven SSL algorithms ; - to propose methods for learning robust and unbiased representations. SSL models are evaluated with downstream task-dependent metrics e.g., word error rate for speech recognition. This couple the evaluation of the universality of SSL representations to a potentially biased and costly fine-tuning that also hides the efficiencyinformation related to the pre-training cost. In practice, we will seek to measure the training efficiency as the ratio between the amount of data, computation and memory needed to observe a certain gain in terms of performance on a metric of interest i.e.,downstream dependent or not. The first step will be to document standard markers that can be used as robust measurements to assess these values robustly at training time. Potential candidates are, for instance, floating point operations for computational intensity, number of neural parameters coupled with precision for storage, online measurement of memory consumption for training and cumulative input sequence length for data. Most state-of-the-art SSL models for speech rely onmasked prediction e.g. HuBERT and WavLM, or contrastive losses e.g. wav2vec 2.0. Such prevalence in the literature is mostly linked to the size, amount of data and computational resources injected by thecompany producing these models. In fact, vanilla masking approaches and contrastive losses may be identified as uninformed solutions as they do not benefit from in-domain expertise. For instance, it has been demonstrated that blindly masking frames in theinput signal i.e. HuBERT and WavLM results in much worse downstream performance than applying unsupervised phonetic boundaries [Yue2021] to generate informed masks. Recently some studies have demonstrated the superiority of an informed multitask learning strategy carefully selecting self-supervised pretext-tasks with respect to a set of downstream tasks, over the vanilla wav2vec 2.0 contrastive learning loss [Zaiem2022]. In this PhD project, our objective is: 1. continue to develop knowledge-driven SSL algorithms reaching higher efficiency ratios and results at the convergence, data consumption and downstream performance levels; and 2. scale these novel approaches to a point enabling the comparison with current state-of-the-art systems and therefore motivating a paradigm change in SSL for the wider speech community. Despite remarkable performance on academic benchmarks, SSL powered technologies e.g. speech and speaker recognition, speech synthesis and many others may exhibit highly unpredictable results once applied to realistic scenarios. This can translate into a global accuracy drop due to a lack of robustness to adversarial acoustic conditions, or biased and discriminatory behaviors with respect to different pools of end users. Documenting and facilitating the control of such aspects prior to the deployment of SSL models into the real-life is necessary for the industrial market. To evaluate such aspects, within the project, we will create novel robustness regularization and debasing techniques along two axes: 1. debasing and regularizing speech representations at the SSL level; 2. debasing and regularizing downstream-adapted models (e.g. using a pre-trained model). To ensure the creation of fair and robust SSL pre-trained models, we propose to act both at the optimization and data levels following some of our previous work on adversarial protected attribute disentanglement and the NLP literature on data sampling and augmentation [Noé2021]. Here, we wish to extend this technique to more complex SSL architectures and more realistic conditions by increasing the disentanglement complexity i.e. the sex attribute studied in [Noé2021] is particularly discriminatory. Then, and to benefit from the expert knowledge induced by the scope of the task of interest, we will build on a recent introduction of task-dependent counterfactual equal odds criteria [Sari2021] to minimize the downstream performance gap observed in between different individuals of certain protected attributes and to maximize the overall accuracy. Following this multi-objective optimization scheme, we will then inject further identified constraints as inspired by previous NLP work [Zhao2017]. Intuitively, constraints are injected so the predictions are calibrated towards a desired distribution i.e. unbiased. *SKILLS* * Master 2 in Natural Language Processing, Speech Processing, computer science or data science. * Good mastering of Python programming and deep learning framework. * Previous in Self-Supervised Learning, acoustic modeling or ASR would be a plus * Very good communication skills in English * Good command of French would be a plus but is not mandatory *SCIENTIFIC ENVIRONMENT* The thesis will be conducted within the Getalp teams of the LIG laboratory (_https://lig-getalp.imag.fr/_ <https://lig-getalp.imag.fr/>) and the LIA laboratory (https://lia.univ-avignon.fr/). The GETALP team and the LIA have a strong expertise and track record in Natural Language Processing and speech processing. The recruited person will be welcomed within the teams which offer a stimulating, multinational and pleasant working environment. The means to carry out the PhD will be providedboth in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and LIA. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments. The PhD position will be co-supervised by Mickael Rouvier (LIA, Avignon) and Benjamin Lecouteux and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited and the partners from LIA, LIG and Dauphine Université PSL, Paris. Furthermore, the project will involve one of the founders of SpeechBrain, Titouan Parcollet with whom the candidate will interact closely. *INSTRUCTIONS FOR APPLYING* Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Mickael Rouvier (_mickael.rouvier(a)univ-avignon.fr_ <mailto:mickael.rouvier@univ-avignon.fr>), Benjamin Lecouteux(benjamin.lecouteux(a)univ-grenoble-alpes.fr) and François Portet (_francois.Portet(a)imag.fr_ <mailto:francois.Portet@imag.fr>). We celebrate diversity and are committed to creating an inclusive environment for all employees. *REFERENCES:* [Noé2021] Noé, P.- G., Mohammadamini, M., Matrouf, D., Parcollet, T., Nautsch, A. & Bonastre, J.- F. Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation in Proc. Interspeech 2021 (2021), 1902–1906. [Sari2021] Sarı, L., Hasegawa-Johnson, M. & Yoo, C. D. Counterfactually Fair Automatic Speech Recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 3515–3525 (2021) [Yue2021] Yue, X. & Li, H. Phonetically Motivated Self-Supervised Speech Representation Learning in Proc. Interspeech 2021 (2021), 746–750. [Zaiem2022] Zaiem, S., Parcollet, T. & Essid, S. Pretext Tasks Selection for Multitask Self-Supervised Speech Representation in AAAI, The 2nd Workshop on Self-supervised Learning for Audio and Speech Processing, 2023 (2022). [Zhao2017] Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K. - W. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017), 2979–2989. -- François PORTET Professeur - Univ Grenoble Alpes Laboratoire d'Informatique de Grenoble - Équipe GETALP Bâtiment IMAG - Office 333 700 avenue Centrale Domaine Universitaire - 38401 St Martin d'Hères FRANCE Phone: +33 (0)4 57 42 15 44 Email:francois.portet@imag.fr www:http://membres-liglab.imag.fr/portet/

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Digital lexicography and lexical computing workshop, Cambridge, UK
by Ondřej Matuška 02 May '23

02 May '23

*<Lexicom/>* a workshop in digital lexicography and lexical computing *SAVE THE DATE* *Jesus College, Cambridge, UK*11 – 15 September 2023 Your 5 days to get up-to-date with the latest developments in *corpus-driven lexicography* and to activate and enhance your *corpus building and corpus query skills* with some of the top experts in the field. For the programme, lecturers, invited speakers, fees and registration, visit this website *lexicom.courses <https://lexicom.courses/lexicom-2023-cambridge-uk-lexicography-workshop/>* I hope to meet you in Cambridge in September! Ondřej *Ondřej Matuška* sketchengine.co.uk <http://www.sketchengine.co.uk> | Facebook <https://www.facebook.com/SketchEngine/> | LinkedIn <https://www.linkedin.com/in/ondrejmatuska> | Twitter <https://twitter.com/SketchEngine>

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[Corpora-List]NLP4CALL 2022 First call for papers
by David Alfter 20 Feb '23

20 Feb '23

== 11th NLP4CALL, Louvain-la-Neuve, Belgium== The workshop series on Natural Language Processing (NLP) for Computer-Assisted Language Learning (NLP4CALL) is a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The latter includes, among others, insights from Second Language Acquisition (SLA) research, on the one hand, and promote development of "Computational SLA" through setting up Second Language research infrastructure(s), on the other. The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings "understanding" of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research – Intelligent CALL, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into second language acquisition theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop invites therefore a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competences from these areas for sharing experiences and brainstorming around the future of the field. We welcome papers: - that describe research directly aimed at ICALL; - that demonstrate actual or discuss the potential use of existing Language and Speech Technologies or resources for language learning; - that describe the ongoing development of resources and tools with potential usage in ICALL, either directly in interactive applications, or indirectly in materials, application or curriculum development, e.g. learning material generation, assessment of learner texts and responses, individualized learning solutions, provision of feedback; - that discuss challenges and/or research agenda for ICALL - that describe empirical studies on language learner data. This year a special focus is given to work done on second language vocabulary and grammar profiling, as well as the use of crowdsourcing for creating, collecting and curating data in NLP projects. We encourage paper presentations and software demonstrations describing the above-mentioned themes primarily, but not exclusively, for the Nordic languages. ==Invited speakers== This year, we have the pleasure to announce two invited talks. The first talk is by Christopher Bryant from Reverso and the University of Cambridge. The second talk is given by Marije Michel from the University of Amsterdam. ==Submission information== Authors are invited to submit long papers (8-12 pages) alternatively short papers (4-7 pages), page count not including references. We will be using the NLP4CALL workshop template for the workshop this year. The author kit, including LaTeX and Microsoft Word templates can be accessed here, alternatively on Overleaf: <https://spraakbanken.gu.se/sites/default/files/2022/NLP4CALL%20workshop%20t…> <https://spraakbanken.gu.se/sites/default/files/2022/nlp4call%20template.doc> <https://www.overleaf.com/latex/templates/nlp4call-workshop-template/qqqzqqy…> Submissions will be managed through the electronic conference management system EasyChair <https://easychair.org/conferences/?conf=nlp4call2022>. Papers must be submitted digitally through the conference management system, in PDF format. Final camera-ready versions of accepted papers will be given an additional page to address reviewer comments. Papers should describe original unpublished work or work-in-progress. Papers will be peer reviewed by at least two members of the program committee in a double-blind fashion. All accepted papers will be collected into a proceedings volume to be submitted for publication in the NEALT Proceeding Series (Linköping Electronic Conference Proceedings) and, additionally, double-published through the ACL anthology, following experiences from the previous NLP4CALL editions (<https://www.aclweb.org/anthology/venues/nlp4call/>). ==Important dates== 7 October 2022: paper submission deadline 4 November 2022: notification of acceptance 25 November 2022: camera-ready papers for publication 9 December 2022: workshop date ==Organizers== David Alfter (1,2), Elena Volodina (2), Thomas François (1), Piet Desmet (3), Frederik Cornillie (3), Arne Jönsson (4), Eveline Rennes (4) (1) CENTAL, Institute for Language and Communication, Université Catholique de Louvain, Belgium (2) Språkbanken, University of Gothenburg, Sweden (3) Itec, Department of Linguistics at KU Leuven & imec, Belgium (4) Department of Computer and Information Science, Linköping University, Sweden ==Contact== For any questions, please contact David Alfter, david.alfter(a)uclouvain.be For further information, see the workshop website <https://spraakbanken.gu.se/en/research/themes/icall/nlp4call-workshop-serie…> Follow us on Twitter @NLP4CALL <https://twitter.com/NLP4CALL/> David Alfter, PhD Post-doctoral researcher Institut Langage et communication, CENTAL Université catholique de Louvain Place Montesquieu, 3 (box L2.06.04) 1348 Louvain-la-Neuve

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OFAI 2022 Lecture Series
by Tristan Miller 09 Nov '22

09 Nov '22

The Austrian Research Institute for Artificial Intelligence (OFAI) is delighted to announce its 2022 Lecture Series, featuring an eclectic lineup of internal and external speakers. The talks are intended to familiarize attendees with the latest research developments in AI and related fields (particularly computational linguistics and natural language processing), and to forge new connections with those working in other areas. Most lectures (see prospective schedule below) will take place on Wednesdays at 18:30 Central European (Summer) Time. All lectures will be held online via Zoom; in-person attendance at OFAI Headquarters in Vienna is also possible for certain lectures. Attendance is open to the public and free of charge. No registration is required. Visit https://www.ofai.at/lectures for full details! 29 June Scott Patterson McGill University Domesticating Wealth Inequality: Hybrid Discourse Analysis of UN General Assembly Speeches, 1971–2018 6 July Pamela Breda Independent artist Feeling for Nonexsistent Beings 13 July Brigitte Krenn OFAI Robots as Social Agents: Between Construct and Reality 20 July Tristan Miller OFAI What's in a Pun? Assessing the Relationship Between Phonological and Semantic Distance and Perceived Funniness of Punning Jokes 27 July Katrien Beuls Université de Namur Unravelling the Computational Mechanisms Underlying the Emergence of Human-like Communication Systems in Populations of Autonomous Agents 7 September Steffen Eger Bielefeld University Text Generation for the Humanities 14 September Antti Arppe University of Alberta Finding Words that Aren't There: Using Word Embeddings to Improve Dictionary Search for Low-resource Languages 21 September Roman Pflugfelder AIT Austrian Institute of Technology Title TBA 28 September Raphael Deimel TU Wien Towards Intuitive Object Handovers Between Humans and Robots 5 October Christoph Scheepers University of Glasgow The “Crossword Effect” in Free Word Recall: A Retrieval Advantage for Words Encoded in Line with their Spatial Associations 12 October Karën Fort Sorbonne Université Title TBA 19 October Benjamin Roth University of Vienna Evaluation and Learning with Structured Test Sets 25 October Peter Hallman OFAI Comparatives in Arabic 2 November Stephanie Gross OFAI Title TBA 9 November Bernhard Pfahringer University of Waikato The World is not IID: Learning from Data Streams to the Rescue 16 November Paolo Petta OFAI Title TBA 23 November Robert Trappl OFAI Title TBA -- Dr.-Ing. Tristan Miller, Research Scientist Austrian Research Institute for Artificial Intelligence (OFAI) Freyung 6/6, 1010 Vienna, Austria | Tel: +43 1 5336112 12 https://logological.org/ | https://punderstanding.ofai.at/

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Journal of Biomedical Informatics (JBI) Special Issue on Semantics-enabled Biomedical Literature Analytics
by Halil Kilicoglu 07 Nov '22

07 Nov '22

*** Apologies for cross-posting *** Call for Papers: Semantics-enabled Biomedical Literature Analytics This Special Issue aims to highlight the development of novel informatics methods for *retrieval, indexing, and analysis of biomedical literature, focusing on semantics-based techniques*. We invite researchers working in biomedical informatics, knowledge representation/ontologies, information retrieval, natural language processing, artificial intelligence/machine learning, data mining, and other related areas to submit clear and detailed descriptions of their novel methodological results. The topics of interest include but are not limited to: - Knowledge representation and semantics for biomedical literature retrieval - Biomedical ontologies in search - Biomedical knowledge source integration - Biomedical knowledge graph construction and embeddings - Knowledge graphs in biomedical search - Semantic knowledge in biomedical literature classification and ranking - Biomedical information extraction - Entity linking and semantic annotation in biomedical texts - Literature-based knowledge discovery - Semantics for biomedical knowledge synthesis and systematic literature review All submitted papers must be original and will go through a rigorous peer-review process with at least two reviewers. Papers previously published in conference proceedings will not be considered. JBI’s editorial policy will be strictly followed by special issue reviewers. Note in particular that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers that focus solely on a specific application are not suitable for JBI. *Submission Guidelines* Authors must submit their papers via the online Editorial Manager (EES) at <http://ees.elsevier.com/jbi>https://www.editorialmanager.com/jbi <https://ees.elsevier.com/jbi>. Authors should select “Semantics-enabled Biomedical Literature Analytics” as their submission category and note in a cover letter that their submission is for the “*Special Issue on Semantics-enabled Biomedical Literature Analytics.*” If the manuscript is not intended as an original research paper, the cover letter should also specify if it is, rather, a *Methodological Review, Commentary, or Special Communication*. Authors should make sure to place their work in the context of human-focused biomedical research or health care, and to review carefully the relevant literature. JBI’s editorial policy, and the types of articles that the journal publishes, are outlined under *Aims and Scope *on the journal home page at https://www.sciencedirect.com/journal/journal-of-biomedical-informatics <https://www.journals.elsevier.com/journal-of-biomedical-informatics>(click on “View full Aims and Scope” for details). All submissions should follow the guidelines for authors at <https://www.elsevier.com/journals/journal-ofbiomedical-%20informatics/1532-…>*https://www.elsevier.com/journals/journal-ofbiomedical- informatics/1532-0464/guide-for-authors <https://www.elsevier.com/journals/journal-ofbiomedical-%20informatics/1532-…>*, including format and manuscript structure. *Important Dates* Deadline for submissions: November 15, 2022 First-round review decisions: January 15, 2023 Deadline for revision submissions: February 15, 2023 Notification of final decisions: April 15, 2023 The full Call for Papers is available at https://doi.org/10.1016/j.jbi.2022.104134. Please direct any questions regarding the special issue to Dr. Halil Kilicoglu (halil(a)illinois.edu). *Guest Editors:* Halil Kilicoglu (University of Illinois Urbana-Champaign, halil(a)illinois.edu ) Faezeh Ensan (Ryerson University, fensan(a)ryerson.ca) Bridget McInnes (Virginia Commonwealth University, bmtinnes(a)vcu.edu) Lucy Lu Wang (University of Washington/Allen Institute for AI, lucylw(a)uw.edu ) --Halil *HALIL KILICOGLU* *Associate Professor* School of Information Sciences University of Illinois at Urbana-Champaign halil(a)illinois.edu https://ischool.illinois.edu/people/halil-kilicoglu

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PhD position in Conversational AI / Multilingual NLP - University of Groningen (NL)
by Bisazza, A. 27 Sep '22

27 Sep '22

We are looking for a PhD candidate in the areas of Natural Language Processing (NLP), Conversational AI and Multilingual NLP. Application Deadline: 2 October 2022 Job description: This 4-year salaried PhD position is embedded in the project “Low-Resource Chat-based Conversational Intelligence (LESSEN <http://lessen-project.nl/>), funded by the Dutch Research Council (NWO). The project’s consortium brings together a diverse set of academic researchers and industrial stakeholders aimed at developing safe and transparent chat-based conversational AI agents, based on state-of-the-art neural architectures. In this context, the selected PhD candidate will work on multilinguality-related challenges and opportunities, with the goal of improving conversational agents under data scarcity conditions. Besides enabling knowledge sharing among high- and low-resource languages and language variants, the project aims at empowering chatbots to handle code-switching utterances, which are common in many communities. Qualifications: We are looking for a motivated and enthusiastic student with a Master degree in computational linguistics, artificial intelligence, computer science, information science, or related areas. Machine learning skills are mandatory; experience with training and/or designing neural networks for language processing tasks is strongly desired; experience in conversational systems is a plus. Furthermore, an excellent knowledge of English and good academic writing skills are essential. Organisation: The PhD candidate will be based at the Centre for Language and Cognition of the University of Groningen (CLCG) and will collaborate with other researchers from the Lessen project. The research will be carried out in the context of the Computational Linguistics group ( https://www.rug.nl/research/clcg/research/cl/) of the CLCG research institute. Application Deadline: 2 October 2022 Start date: 1 January 2023 Find all details and apply here: https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… For questions you can contact: Dr A. Bisazza, a.bisazza(a)rug.nl Please do NOT use the e-mail address above for applications. Please first check if your question is already answered at the application link <https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0…> . -- Arianna Bisazza Assistant Professor University of Groningen http://www.cs.rug.nl/~bisazza

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3-year postdoc position in Large-Scale Natural Language Processing at the University of Oslo, Norway
by Andrey Kutuzov 20 Sep '22

20 Sep '22

[with apologies for cross-posting] The Language Technology Group (LTG) at the University of Oslo offers a fully funded postdoctoral fellowship for a duration of 3 years. This position is a part of a new EU Horizon project titled “High-Performance Language Technologies” (HPLT). HPLT is a collaboration between 5 universities (Oslo, Edinburgh, Helsinki, Prague and Turku), 2 high-performance computing centers (Uninett Sigma2 in Norway and Cesnet in the Czech Republic), and one Spanish company (Prompsit) on the development of language and translation models at scale. The project aims at continuous integration of pre-trained language models and data, resulting in free downloadable high-quality models for all official European Union languages and beyond. With a strong focus on multi-linguality, reproducibility, openness and scale, HPLT will allow easy discovery and access to corpora, models and code. We are looking for a postdoctoral fellow interested in processing and maintaining very large troves of natural language data (in particular, significant parts of the Internet Archive). These corpora and datasets will be used for training, updating, evaluating and publicly serving state-of-the-art language models for European languages (both monolingual and multilingual). This includes developing and refining automated replicable software installations across high-performance computing (HPC) systems, notably the new LUMI European pre-exascale supercomputer. The successful candidate will join the HPLT project team and enjoy an active role in the consortium. Applicants must hold a degree equivalent to a Norwegian doctoral degree in Natural Language Processing, Computational Linguistics, or Computer Science with a suitable specialization. Candidates whose doctoral thesis has been submitted for evaluation by the closing date are encouraged to apply. Only applicants with an approved doctoral thesis and public defense prior to the start date will be eligible for appointment. The application deadline is September 23, 2022. Starting date subject to discussion, but no later than February 1, 2023. For more information, please see the full announcement and application form here: https://www.jobbnorge.no/en/available-jobs/job/231118/postdoctoral-research… Please do not hesitate to contact us for any further information: - postdoctoral fellow Andrey Kutuzov: andreku(a)ifi.uio.no - professor Stephan Oepen: oe(a)ifi.uio.no -- Andrey Postdoctoral Fellow Language Technology Group (LTG) University of Oslo

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Jobs: Tenure Track Faculty, Rochester Institute of Technology
by Cecilia Alm 01 Sep '22

01 Sep '22

The Department of Psychology at the Rochester Institute of Technology (RIT; www.rit.edu/psychology<http://www.rit.edu/psychology>) invites candidates to apply for a tenure-track Assistant Professor position starting in August 2023. We are seeking two energetic and enthusiastic psychologists who will serve as instructors, researchers, and mentors to students in our undergraduate (Psychology, Neuroscience) and graduate (Masters in Experimental Psychology, Ph.D. in Cognitive Science) programs. We are particularly looking to build a cohort who can contribute to the new interdisciplinary Ph.D. program in Cognitive Science at RIT. Candidates should have expertise in cognitive psychology, comparative psychology, cognitive or behavioral neuroscience, AI, computational/psycho-linguistics or related areas. The position requires a strong commitment to teaching, active research and publication, and a strong potential to attract external funding. Teaching and research are priorities for faculty at RIT, and all faculty are expected to mentor students through advising, research and in class experiences. The successful candidates will be able to teach courses in our undergraduate cognitive psychology track (Memory & Attention, Language & Thought, Decision Making, Judgement & Problem Solving), be able to teach research methods/statistics courses at the undergraduate and graduate level, and teach and mentor students in our Cognitive Science Ph.D. program. In addition, candidates must be able to do research and work effectively within the department’s existing lab space. RIT provides many opportunities for collaborative research across the institute in many diverse disciplines such as AI, Digital Humanities, Human-Centered Computing, and Cybersecurity. We are seeking an individual who has the ability and interest in contributing to RIT’s core values<http://www.rit.edu/academicaffairs/policiesmanual/p040>, honor code<http://www.rit.edu/academicaffairs/policiesmanual/p030>, and statement of diversity.<http://www.rit.edu/academicaffairs/policiesmanual/p050> REQUIRED MINIMUM QUALIFICATIONS: • Have PhD., or PhD. expected by July 1, 2023 in cognitive psychology or cognitive science related specialty; • Have demonstrated ability to conduct independent research in psychology; • Have consistently and recently published; • Have demonstrated teaching ability and have taught college courses independently beyond TA; • Have demonstrated ability to supervise student research; • Demonstrate external research grant attainment potential; • Demonstrate expertise in research and teaching in cognitive science; • Show a career trajectory that emphasizes a balance between teaching and research; • Show a fit with the Department of Psychology’s general mission, teaching, research, and resources. • Ability to contribute in meaningful ways to the college’s continuing commitment to cultural diversity, pluralism, and individual differences. HOW TO APPLY: Apply online at http://careers.rit.edu/faculty; search openings, then Keyword Search 7153BR. Please submit your application, curriculum vitae, cover letter addressing the listed qualifications and upload the following attachments: • A brief teaching philosophy • A research statement that includes information about previous grant work, the potential for future grants, and information about one-on-one supervision of student research • The names, addresses and phone numbers for three references • Contribution to Diversity Statement<https://www.rit.edu/academicaffairs/facultyrecruitment/forms/Diversity_Stat…> You can contact the chair of the search committee, Andrew M. Herbert, Ph.D. with questions on the position at: amhgss(a)rit.edu<mailto:amhgss@rit.edu>. Review of applications will begin October 1, 2022 and will continue until an acceptable candidate is found. RIT does not discriminate. RIT is an equal opportunity employer that promotes and values diversity, pluralism, and inclusion. For more information or inquiries, please visit RIT/TitleIX or the U.S. Department of Education at ED.Gov Cecilia O. Alm, Ph.D. She/Her Joint Program Director, MS Program in Artificial Intelligence, School of Information Director, AWARE-AI NSF Research Traineeship Program - rit.edu/nrtai/<http://rit.edu/nrtai/> Associate Director, Center for Human-aware AI - rit.edu/chai/<http://rit.edu/chai/> Professor, Department of Psychology, CLA, Rochester Institute of Technology Affiliated with Department of Computer Science, Ph.D. Program in Computer and Information Sciences, MS Program in Data Science 92 Lomb Memorial Drive Rochester NY 14623 people.rit.edu/coagla, rit.edu/clasp, cs.rit.edu/~reu, rit.edu/nlp<https://people.rit.edu/coagla,%20rit.edu/clasp,%20cs.rit.edu/~reu,%20rit.ed…> W: 585-475-7327 M: 585-536-7539 coagla(a)rit.edu<mailto:coagla@rit.edu>

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PhD Position in Speech Translation at Fondazione Bruno Kessler: application deadline on September 6
by Matteo Negri 31 Aug '22

31 Aug '22

-- Apologies for cross-posting. -- IN A NUTSHELL: less than 1 week to the application deadline for a fully-funded #PhDposition in "Application-oriented Speech Translation" at Fondazione Bruno Kessler. -- Scholarship "A6": http://iecs.unitn.it/education/admission/reserved-topic-scholarships#A6 -- Application info: https://iecs.unitn.it/education/admission/call-for-application -- Deadline: September 6, 2022 FULL MESSAGE: Have you recently completed or expect very soon an MSc or equivalent degree in computer science, artificial intelligence, computational linguistics, engineering, or a related area? Are you interested in carrying out research on Speech Translation during the next few years? Are you excited to spend a part of your life in a pleasant city in the heart of the Italian Alps? WE ARE LOOKING FOR YOU!!! The Machine Translation <https://ict.fbk.eu/units/hlt-mt/> (MT) group at Fondazione Bruno Kessler (Trento, Italy) in conjunction with the ICT International Doctorate School of the University of Trento <https://iecs.unitn.it/> is pleased to announce the availability of the following fully-funded Ph.D. position in Speech Translation. TITLE: Application-oriented Speech Translation DESCRIPTION: The need to translate audio input from one language into text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems are now required to be flexible and robust enough to operate in different application scenarios. On one side, the industry calls for key features like real-time processing, domain adaptability, extended language coverage, and the capability to meet application-specific constraints. On the other side, society calls for new efforts towards inclusiveness with respect to specific categories and groups (e.g. gender-sensitivity, customization to the needs of impaired users). Both industry and society face the orthogonal challenges posed by the variability of audio conditions (e.g. background noise, strong speakers’ accent, overlapping speakers). The objective of this Ph.D. is to make ST flexible and robust to these and other factors. CONTACT: negri(a)fbk.eu COMPLETE DETAILS AVAILABLE AT: https://iecs.unitn.it/education/admission/call-for-application IMPORTANT DATES: The deadline for application is September 6, 2022, hrs. 04:00 PM (CEST) Potential candidates are strongly invited to contact us in advance for preliminary interviews. Precedence for interviews will be given to short-listed candidates that will send us a complete CV via email ( negri(a)fbk.eu) by September 10, 2022. Candidate profile The ideal candidate must have recently completed or expect very soon an MSc or equivalent degree in computer science, artificial intelligence, computational linguistics, engineering, or a closely related area. In addition, the applicant should: - Have interest in Machine and Speech Translation - Have experience in deep learning and machine learning, in general - Have good programming skills in Python and experience in PyTorch - Enjoy working with real-world problems and large data sets - Have good knowledge of written and spoken English - Enjoy working in a closely collaborating team Working Environment The doctoral student will be employed at the MT group at Fondazione Bruno Kessler, Trento, Italy. The group (about 10 people including staff and students) has a long tradition in research on machine and speech translation and is currently involved in several projects. Former students are nowadays employed in leading IT companies in the world. Benefits Fondazione Bruno Kessler offers an attractive benefits package, including a flexible work week, full reimbursement for conferences and summer schools, a competitive salary, an excellent team of supervisors and mentors, help for housing, full health insurance, the possibility of Italian courses, and sporting facilities. Further Information For preliminary interviews, and should you need further information about the position, please contact Matteo Negri (negri(a)fbk.eu). Best Regards, Matteo Negri -- -- Le informazioni contenute nella presente comunicazione sono di natura privata e come tali sono da considerarsi riservate ed indirizzate esclusivamente ai destinatari indicati e per le finalità strettamente legate al relativo contenuto. Se avete ricevuto questo messaggio per errore, vi preghiamo di eliminarlo e di inviare una comunicazione all’indirizzo e-mail del mittente. -- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. If you received this in error, please contact the sender and delete the material.

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Second Workshop on Generation Evaluation and Metrics at EMNLP 2022: 2nd Call for Papers
by kdhole＠emory.edu 31 Aug '22

31 Aug '22

This is the 2nd call for papers for The Second Version of Generation, Evaluation And Metrics (GEM) Workshop 2022 workshop which will be held as part of EMNLP, December 7-11, 2022. It is endorsed by the ACL Special Interest Group on Natural Language Generation (SIGGEN). ======== Overview ======== Natural language generation (NLG) is one of the most active research fields in NLP. Yet, much of the work is focused on English, and too little attention is given to evaluation processes. As many of the recent developments in few-shot and in-context learning have led to the treatment of many tasks as text generation problems, the need for better NLG evaluation processes is becoming more urgent. To that end, the GEM workshop aims to encourage the development of (semi-) automatic model audits and improved human evaluation strategies, and to popularize model evaluations in languages beyond English. We welcome submissions related, but not limited to, the following topics: - Automatic evaluation of NLG systems - Creating NLG corpora and challenge sets - Critiques of benchmarking efforts and responsibly measuring progress in NLG - Effective and/or efficient NLG methods that can be applied to a wide range of languages and tasks - Standardizing human evaluation and making it more robust Check examples here: https://www.aclweb.org/portal/content/second-workshop-generation-evaluation… We additionally invite submissions that conduct in-depth analyses of outputs of existing systems, for example through error analyses, by applying new metrics, or by testing the system on new test sets. While we encourage the use of the infrastructure the organizing team is developing as part of the GEM benchmark, its use is not required. If you are interested in seeing last year's workshop website from ACL 2021, please check here. ======== How to submit? ======== Submissions can take either of the following forms: - Archival Papers Papers describing original and unpublished work can be submitted in a between 4 and 8 page format. - Non-Archival Abstracts To discuss work already presented or under review at a peer-reviewed venue, we allow the submission of 2-page abstracts. All submissions are allowed unlimited space for references and appendices and should conform to EMNLP 2022 style guidelines. Archival paper submissions must be anonymized while abstract submissions may include author information. You can either commit a paper reviewed through ARR at here or submit directly through SoftConf here. Note that there are separate deadlines for the two options (see below) We additionally invite presentations by authors of papers in the Findings of the EMNLP (details to be announced at a later date). ======== Shared Task ======== We are organizing a shared task focused on multilingual summarization, including human and automatic evaluation. Participants of the shared task are invited to submit a system description of 4-8 pages. ======== Important Dates (Submission Date Revised from 7 Sept to 21 Sept) ======== Paper Submission Dates - 21 September 2022: Workshop paper submission deadline if using Softconf - 2 October 2022: Latest ARR commitment deadline - 16 October 2022: Workshop paper notification deadline - 23 October 2022: Workshop paper camera ready deadline Workshop Dates - 7-8 December 2022: Workshops ======== Organization ======== - Antoine Bosselut (EPFL) - Khyathi Chandu (Carnegie Mellon University) - Kaustubh Dhole (Emory University) - Varun Gangal (Carnegie Mellon University) - Sebastian Gehrmann (Google Research) - Yacine Jernite (Hugging Face) - Jekaterina Novikova (NoOverfitting Lab) - Laura Perez-Beltrachini (University of Edinburgh) Steering Committee - Wei Xu (Georgia Tech) - Esin Durmus (Stanford University) - Samira Shaikh (UNC Charlotte) Website: https://gem-benchmark.com/workshop Submission Deadline: Wednesday, 21 September 2022 Contact Email: gehrmann(a)google.com gem-benchmark(a)googlegroups.com

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2024

2023

2022

Corpora August 2022