Representations of Meaning in Neural Networks for NLP: a Thesis Proposal

no code implementations • NAACL 2021 • Tom{\'a}{\v{s}} Musil

Neural networks are the state-of-the-art method of machine learning for many problems in NLP.

Machine Translation Philosophy +1

A Test Suite and Manual Evaluation of Document-Level NMT at WMT19

no code implementations • WS 2019 • Kate{\v{r}}ina Rysov{\'a}, Magdal{\'e}na Rysov{\'a}, Tom{\'a}{\v{s}} Musil, Lucie Pol{\'a}kov{\'a}, Ond{\v{r}}ej Bojar

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.

Machine Translation NMT +2

Derivational Morphological Relations in Word Embeddings

no code implementations • WS 2019 • Tom{\'a}{\v{s}} Musil, Jon{\'a}{\v{s}} Vidra, David Mare{\v{c}}ek

Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes.

Clustering Word Embeddings

Neural Monkey: The Current State and Beyond

no code implementations • WS 2018 • Jind{\v{r}}ich Helcl, Jind{\v{r}}ich Libovick{\'y}, Tom Kocmi, Tom{\'a}{\v{s}} Musil, Ond{\v{r}}ej C{\'\i}fka, Du{\v{s}}an Vari{\v{s}}, Ond{\v{r}}ej Bojar

Image Captioning Machine Translation +3

Results of the WMT17 Neural MT Training Task

no code implementations • WS 2017 • Ond{\v{r}}ej Bojar, Jind{\v{r}}ich Helcl, Tom Kocmi, Jind{\v{r}}ich Libovick{\'y}, Tom{\'a}{\v{s}} Musil

Machine Translation