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| = Natural Language Processing Seminar 2016–2017 = | = Natural Language Processing Seminar 2020–2021 = |
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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.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). All recorded talks are available [[https://www.youtube.com/channel/UC5PEPpMqjAr7Pgdvq0wRn0w|on 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">'''3 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">'''5 October 2020'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Piotr Rybak''', '''Robert Mroczkowski''', '''Janusz Tracz''' (ML Research at Allegro.pl), '''Ireneusz Gawlik''' (ML Research at Allegro.pl & AGH University of Science and Technology)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Review of BERT-based Models for Polish Language'''  {{attachment:seminarium-archiwum/icon-pl.gif|Delivered in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">In recent years, a series of BERT-based models improved the performance of many natural language processing systems. During this talk, we will briefly introduce the BERT model as well as some of its variants. Next, we will focus on the available BERT-based models for Polish language and their results on the KLEJ benchmark. Finally, we will dive into the details of the new model developed in cooperation between ICS PAS and Allegro.|| |
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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|2015-16]].|| | {{{#!wiki comment ||<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.|| }}} ||<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|2015–2020]].|| |
Natural Language Processing Seminar 2020–2021
The NLP Seminar is organised by the Linguistic Engineering Group at the Institute of Computer Science, 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). All recorded talks are available on YouTube. |
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5 October 2020 |
Piotr Rybak, Robert Mroczkowski, Janusz Tracz (ML Research at Allegro.pl), Ireneusz Gawlik (ML Research at Allegro.pl & AGH University of Science and Technology) |
Review of BERT-based Models for Polish Language |
In recent years, a series of BERT-based models improved the performance of many natural language processing systems. During this talk, we will briefly introduce the BERT model as well as some of its variants. Next, we will focus on the available BERT-based models for Polish language and their results on the KLEJ benchmark. Finally, we will dive into the details of the new model developed in cooperation between ICS PAS and Allegro. |
Please see also the talks given in 2000–2015 and 2015–2020. |