Tomáš Musil


2021

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Representations of Meaning in Neural Networks for NLP: a Thesis Proposal
Tomáš Musil
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Neural networks are the state-of-the-art method of machine learning for many problems in NLP. Their success in machine translation and other NLP tasks is phenomenal, but their interpretability is challenging. We want to find out how neural networks represent meaning. In order to do this, we propose to examine the distribution of meaning in the vector space representation of words in neural networks trained for NLP tasks. Furthermore, we propose to consider various theories of meaning in the philosophy of language and to find a methodology that would enable us to connect these areas.

2019

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Derivational Morphological Relations in Word Embeddings
Tomáš Musil | Jonáš Vidra | David Mareček
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes. In this paper, we explore the potential of word embeddings to identify properties of word derivations in the morphologically rich Czech language. We extract derivational relations between pairs of words from DeriNet, a Czech lexical network, which organizes almost one million Czech lemmas into derivational trees. For each such pair, we compute the difference of the embeddings of the two words, and perform unsupervised clustering of the resulting vectors. Our results show that these clusters largely match manually annotated semantic categories of the derivational relations (e.g. the relation ‘bake–baker’ belongs to category ‘actor’, and a correct clustering puts it into the same cluster as ‘govern–governor’).

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A Test Suite and Manual Evaluation of Document-Level NMT at WMT19
Kateřina Rysová | Magdaléna Rysová | Tomáš Musil | Lucie Poláková | Ondřej Bojar
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

As the quality of machine translation rises and neural machine translation (NMT) is moving from sentence to document level translations, it is becoming increasingly difficult to evaluate the output of translation systems. We provide a test suite for WMT19 aimed at assessing discourse phenomena of MT systems participating in the News Translation Task. We have manually checked the outputs and identified types of translation errors that are relevant to document-level translation.

2018

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Neural Monkey: The Current State and Beyond
Jindřich Helcl | Jindřich Libovický | Tom Kocmi | Tomáš Musil | Ondřej Cífka | Dušan Variš | Ondřej Bojar
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2017

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Results of the WMT17 Neural MT Training Task
Ondřej Bojar | Jindřich Helcl | Tom Kocmi | Jindřich Libovický | Tomáš Musil
Proceedings of the Second Conference on Machine Translation