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  • THEaiTRobot 2.0

    The THEaiTRobot 2.0 tool allows the user to interactively generate scripts for individual theatre play scenes. The previous version of the tool (http://hdl.handle.net/11234/1-3507) was based on GPT-2 XL generative language model, using the model without any fine-tuning, as we found that with a prompt formatted as a part of a theatre play script, the model usually generates continuation that retains the format. The current version also uses vanilla GPT-2 by default, but can also instead use a GPT-2 medium model fine-tuned on theatre play scripts (as well as film and TV series scripts). Apart from the basic "flat" generation using a theatrical starting prompt and the script model, the tool also features a second, hierarchical variant, where in the first step, a play synopsis is generated from its title using a synopsis model (GPT-2 medium fine-tuned on synopses of theatre plays, as well as film, TV series and book synopses). The synopsis is then used as input for the second stage, which uses the script model. The choice of models to use is done by setting the MODEL variable in start_server.sh and start_syn_server.sh THEaiTRobot 2.0 was used to generate the second THEaiTRE play, "Permeation/Prostoupení".

  • THEaiTRobot 1.0

    The THEaiTRobot 1.0 tool allows the user to interactively generate scripts for individual theatre play scenes. The tool is based on GPT-2 XL generative language model, using the model without any fine-tuning, as we found that with a prompt formatted as a part of a theatre play script, the model usually generates continuation that retains the format. We encountered numerous problems when generating the script in this way. We managed to tackle some of the problems with various adjustments, but some of them remain to be solved in a future version. THEaiTRobot 1.0 was used to generate the first THEaiTRE play, "AI: Když robot píše hru" ("AI: When a robot writes a play").

  • Slavic Forest, Norwegian Wood (scripts)

    Tools and scripts used to create the cross-lingual parsing models submitted to VarDial 2017 shared task (https://bitbucket.org/hy-crossNLP/vardial2017), as described in the linked paper. The trained UDPipe models themselves are published in a separate submission (https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1971). For each source (SS, e.g. sl) and target (TT, e.g. hr) language, you need to add the following into this directory: - treebanks (Universal Dependencies v1.4): SS-ud-train.conllu TT-ud-predPoS-dev.conllu - parallel data (OpenSubtitles from Opus): OpenSubtitles2016.SS-TT.SS OpenSubtitles2016.SS-TT.TT !!! If they are originally called ...TT-SS... instead of ...SS-TT..., you need to symlink them (or move, or copy) !!! - target tagging model TT.tagger.udpipe All of these can be obtained from https://bitbucket.org/hy-crossNLP/vardial2017 You also need to have: - Bash - Perl 5 - Python 3 - word2vec (https://code.google.com/archive/p/word2vec/); we used rev 41 from 15th Sep 2014 - udpipe (https://github.com/ufal/udpipe); we used commit 3e65d69 from 3rd Jan 2017 - Treex (https://github.com/ufal/treex); we used commit d27ee8a from 21st Dec 2016 The most basic setup is the sl-hr one (train_sl-hr.sh): - normalization of deprels - 1:1 word-alignment of parallel data with Monolingual Greedy Aligner - simple word-by-word translation of source treebank - pre-training of target word embeddings - simplification of morpho feats (use only Case) - and finally, training and evaluating the parser Both da+sv-no (train_ds-no.sh) and cs-sk (train_cs-sk.sh) add some cross-tagging, which seems to be useful only in specific cases (see paper for details). Moreover, cs-sk also adds more morpho features, selecting those that seem to be very often shared in parallel data. The whole pipeline takes tens of hours to run, and uses several GB of RAM, so make sure to use a powerful computer.