A simple way of browsing CoNLL format files in your terminal. Fast and text-based.
To open a CoNLL file, simply run: ./view_conll sample.conll
The output is piped through less, so you can use less commands to navigate the
file; by default the less searches for sentence beginnings, so you can use "n"
to go to next sentence and "N" to go to previous sentence. Close by "q". Trees
with a high number of non-projective edges may be difficult to read, as I have
not found a good way of displaying them intelligibly.
If you are on Windows and don't have less (but have Python), run like this: python view_conll.py sample.conll
For complete instructions, see the README file.
You need Python 2 to run the viewer.
The latinpipe-evalatin24-240520 is a PhilBerta-based model for LatinPipe 2024 <https://github.com/ufal/evalatin2024-latinpipe>, performing tagging, lemmatization, and dependency parsing of Latin, based on the winning entry to the EvaLatin 2024 <https://circse.github.io/LT4HALA/2024/EvaLatin> shared task. It is released under the CC BY-NC-SA 4.0 license.
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").
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".
AMALACH project component TMODS:ENG-CZE; machine translation of queries from Czech to English. This archive contains models for the Moses decoder (binarized, pruned to allow for real-time translation) and configuration files for the MTMonkey toolkit. The aim of this package is to provide a full service for Czech->English translation which can be easily utilized as a component in a larger software solution. (The required tools are freely available and an installation guide is included in the package.)
The translation models were trained on CzEng 1.0 corpus and Europarl. Monolingual data for LM estimation additionally contains WMT news crawls until 2013.
En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2020 (BLEU):
en->de: 25.9
de->en: 33.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2020 (BLEU):
en->ru: 18.0
ru->en: 30.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
Tree Editor
TrEd is a fully customizable and programmable graphical editor and viewer for tree-like structures. Among other projects, it was used as the main annotation tool for syntactical and tectogrammatical annotations in The Prague Dependency Treebank, as well as for decision-tree based morphological annotation of The Prague Arabic Dependency Treebank.
The datasets described in Droganova, Kira, and Daniel Zeman. "Towards a Unified Taxonomy of Deep Syntactic Relations." Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 2024.
Four languages are included in this release. English PropBank is omitted due to its license terms.
Treex::Web is a web frontend for running Treex applications from your browser.
Treex (formerly TectoMT) is a highly modular NLP framework implemented in Perl programming language. It is primarily aimed at Machine Translation, making use of the ideas and technology created during the Prague Dependency Treebank project.