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, currently online – please use the link next to the presentation title. 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) |
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. |
2 November 2020 |
Inez Okulska (NASK National Research Institute) |
The introduction of the vector representation of words, containing the weights of context and central words, calculated as a result of mapping giant corpora of a given language, and not encoding manually selected, linguistic features of words, proved to be a breakthrough for NLP research. After the first delight, there came revision and search for improvements - primarily in order to broaden the context, to handle homonyms, etc. Nevertheless, the classic embeddinga still apply to many tasks - for example, content classification - and in many cases their performance is still good enough. What do they code? Do they contain redundant elements? If transformed or reduced, will they maintain the information in a way that still preserves the original "meaning"? What is the meaning here? How far can these vectors be deformed and how does it relate to encryption methods? In my speech I will present a reflection on this subject, illustrated by the results of various "tortures” of the embeddings (word2vec and glove) and their precision in the task of classifying texts whose content must remain masked for human users. |
16 November 2020 |
Agnieszka Chmiel (Adam Mickiewicz University), Danijel Korzinek (Polish-Japanese Academy of Information Technology) |
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PINC is the first Polish simultaneous interpreting corpus based on Polish-English and English-Polish interpretations from the European Parliament. Using naturalistic data makes it possible to answer many questions about the process of simultaneous interpreting. By analysing the ear-voice span, or the delay between the source text and the target text, mechanisms of activation and inhibition can be investigated in the interpreter’s lexical processing. Fluency and pause data help us examine the cognitive load. This talk will focus on how we process data in the corpus (such as interpreter voice identification) and what challenges we face in relation to linguistic analysis, dependency parsing and bilingual alignment. We will show how specific data can be applied to help us understand what interpreting involves or even what happens in the interpreter’s mind. |
14 December 2020 |
Piotr Przybyła (Linguistic Engineering Group, Institute of Computer Science, Polish Academy of Sciences) |
Multi-Word Lexical Simplification |
The presentation will cover the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, a corpus (MWLS1) including 1462 sentences in English from various sources with 7059 simplifications was prepared through crowdsourcing. Additionally, an automatic solution (Plainifier) for the problem, based on a purpose-trained neural language model, will be discussed along with the evaluation, comparing to human and resource-based baselines. The results of the presented study were also published at the COLING 2020 conference in an article of the same title. |
in the seminar room of the ICS PAS (ul. Jana Kazimierza 5, Warszawa)
Please see also the talks given in 2000–2015 and 2015–2020. |
2 April 2020
Stan Matwin (Dalhousie University)
Efficient training of word embeddings with a focus on negative examples
This presentation is based on our AAAI 2018 and 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.