no code implementations • ACL 2017 • Jind{\v{r}}ich Libovick{\'y}, Jind{\v{r}}ich Helcl
Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities.
no code implementations • EMNLP 2018 • Jind{\v{r}}ich Libovick{\'y}, Jind{\v{r}}ich Helcl
Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time.
no code implementations • WS 2018 • Jind{\v{r}}ich Helcl, Jind{\v{r}}ich Libovick{\'y}, Du{\v{s}}an Vari{\v{s}}
For our submission, we acquired both textual and multimodal additional data.
no code implementations • WS 2018 • Jind{\v{r}}ich Libovick{\'y}, Jind{\v{r}}ich Helcl, David Mare{\v{c}}ek
In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways.
no code implementations • WS 2020 • Jind{\v{r}}ich Libovick{\'y}, Zden{\v{e}}k Kasner, Jind{\v{r}}ich Helcl, Ond{\v{r}}ej Du{\v{s}}ek
While the use of additional data and our classifier filter were able to improve results, the paraphrasing model produced too many invalid outputs to further improve the output quality.