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| = Natural Language Processing Seminar 2016–2017 = | = Natural Language Processing Seminar 2025–2026 = |
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| ||<style="border:0;padding:0">The NLP Seminar is organised by the [[http://nlp.ipipan.waw.pl/|Linguistic Engineering Group]] at the [[http://www.ipipan.waw.pl/en/|Institute of Computer Science]], [[http://www.pan.pl/index.php?newlang=english|Polish Academy of Sciences]] (ICS PAS). It takes place on (some) Mondays, normally at 10:15 am, in the seminar room of the ICS PAS (ul. Jana Kazimierza 5, Warszawa). ||<style="border:0;padding-left:30px">[[seminarium|{{attachment:seminar-archive/pl.png}}]]|| | ||<style="border:0;padding-bottom:10px">The NLP Seminar is organised by the [[http://nlp.ipipan.waw.pjl/|Linguistic Engineering Group]] at the [[http://www.ipipan.waw.pl/en/|Institute of Computer Science]], [[http://www.pan.pl/index.php?newlang=english|Polish Academy of Sciences]] (ICS PAS). It will restart in October and will take place on (some) Mondays, usually at 10:15 am, often online – please use the link next to the presentation title. All recorded talks are available on [[https://www.youtube.com/ipipan|YouTube]]. ||<style="border:0;padding-left:30px">[[seminarium|{{attachment:seminar-archive/pl.png}}]]|| |
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| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''10 October 2016'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Katarzyna Pakulska''', '''Barbara Rychalska''', '''Krystyna Chodorowska''', '''Wojciech Walczak''', '''Piotr Andruszkiewicz''' (Samsung)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Paraphrase Detection Ensemble – !SemEval 2016 winner'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk delivered in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">This seminar describes the winning solution designed for a core track within the !SemEval 2016 English Semantic Textual Similarity (STS) task. The goal of the competition was to measure semantic similarity between two given sentences on a scale from 0 to 5. At the same time the solution should replicate human language understanding. The presented model is a novel hybrid of recursive auto-encoders from deep learning (RAE) and a !WordNet award-penalty system, enriched with a number of other similarity models and features used as input for Linear Support Vector Regression.|| |
||<style="border:0;padding-top:5px;padding-bottom:5px">'''15 September 2025'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Louis Esteve''' (Universite Paris-Saclay) || ||<style="border:0;padding-left:30px;padding-bottom:5px">[[https://zil.ipipan.waw.pl/seminarium-online|{{attachment:seminarium-archiwum/teams.png}}]] '''Diversity and dataset size – a quantitative perspective'''  {{attachment:seminarium-archiwum/icon-en.gif|Talk in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">The field of Natural Language Processing (NLP) studies the abilities of computer systems to process and generate natural language, and has received increasing attention from the general population since the democratisation of generative and conversational models. However, behind the scenes, state-of-the-art NLP models are trained on ever-larger datasets, reaching trillions of tokens. It may be argued that the creation and use of such immense datasets is motivated by the idea that 'the larger the dataset, the more diverse it is', and that in turn 'if the training set is more diverse, it shall yield better models'. However, these statements thus far remain intuitions and need to be properly tested. To this end, this presentation will tackle methods and caveats of formal diversity quantification including limitations of the literature, a preliminary discussion on the link between diversity and dataset size, as well as their impact on downstream applications.|| |
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| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''24 October 2016'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Adam Przepiórkowski, Jakub Kozakoszczak, Jan Winkowski, Daniel Ziembicki, Tadeusz Teleżyński''' (Institute of Computer Science, Polish Academy of Sciences / University of Warsaw)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Corpus of formalized steps of textual entailment'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk delivered in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">Description will be available shortly.|| |
||<style="border:0;padding-top:10px">Please see also [[http://nlp.ipipan.waw.pl/NLP-SEMINAR/previous-e.html|the talks given in 2000–2015]] and [[http://zil.ipipan.waw.pl/seminar-archive|2015–2025]].|| |
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| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''7 listopada 2016'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Norbert Ryciak, Aleksander Wawer''' (Institute of Computer Science, Polish Academy of Sciences)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Using recursive deep neural networks and syntax to compute phrase semantics'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk delivered in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">Description will be available shortly.|| |
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| ||<style="border:0;padding-top:10px">Please see also [[http://nlp.ipipan.waw.pl/NLP-SEMINAR/previous-e.html|the talks given between 2000 and 2015]] and [[http://zil.ipipan.waw.pl/seminar-archive|2015-16]].|| | ||<style="border:0;padding-top:5px;padding-bottom:5px">'''11 March 2024'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Mateusz Krubiński''' (Charles University in Prague)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">[[http://zil.ipipan.waw.pl/seminarium-online|{{attachment:seminarium-archiwum/teams.png}}]] '''Talk title will be given shortly'''  {{attachment:seminarium-archiwum/icon-en.gif|Talk in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">Talk summary will be made available soon.|| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''2 April 2020'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Stan Matwin''' (Dalhousie University)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Efficient training of word embeddings with a focus on negative examples'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk delivered in Polish.}} {{attachment:seminarium-archiwum/icon-en.gif|Slides in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">This presentation is based on our [[https://pdfs.semanticscholar.org/1f50/db5786913b43f9668f997fc4c97d9cd18730.pdf|AAAI 2018]] and [[https://aaai.org/ojs/index.php/AAAI/article/view/4683|AAAI 2019]] papers on English word embeddings. In particular, we examine the notion of “negative examples”, the unobserved or insignificant word-context co-occurrences, in spectral methods. we provide a new formulation for the word embedding problem by proposing a new intuitive objective function that perfectly justifies the use of negative examples. With the goal of efficient learning of embeddings, we propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.3 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin. We will round up our discussion with some general thought s about the use of embeddings in modern NLP.|| }}} |
Natural Language Processing Seminar 2025–2026
The NLP Seminar is organised by the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences (ICS PAS). It will restart in October and will take place on (some) Mondays, usually at 10:15 am, often online – please use the link next to the presentation title. All recorded talks are available on YouTube. |
|
15 September 2025 |
Louis Esteve (Universite Paris-Saclay) |
|
The field of Natural Language Processing (NLP) studies the abilities of computer systems to process and generate natural language, and has received increasing attention from the general population since the democratisation of generative and conversational models. However, behind the scenes, state-of-the-art NLP models are trained on ever-larger datasets, reaching trillions of tokens. It may be argued that the creation and use of such immense datasets is motivated by the idea that 'the larger the dataset, the more diverse it is', and that in turn 'if the training set is more diverse, it shall yield better models'. However, these statements thus far remain intuitions and need to be properly tested. To this end, this presentation will tackle methods and caveats of formal diversity quantification including limitations of the literature, a preliminary discussion on the link between diversity and dataset size, as well as their impact on downstream applications. |
Please see also the talks given in 2000–2015 and 2015–2025. |
