We defined 58 dramatic situations and annotated them in 19 play scripts. Then we selected only 5 well-recognized dramatic situations and annotated further 33 play scripts. In this version of the data, we release only play scripts that can be freely distributed, which is 9 play scripts. One play is annotated independently by three annotators.
We defined 58 dramatic situations and annotated them in 19 play scripts. Then we selected only 5 well-recognized dramatic situations and annotated further 33 play scripts. In the previous (first) version, we released 9 play scripts that could be freely distributed. In this (second) version of the data, we are adding another 10 plays for which we have obtained licenses from authors. In total, there are 19 play scripts available, and one of them is annotated three times - independently by three annotators.
This is a document-aligned parallel corpus of English and Czech abstracts of scientific papers published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague, as reported in the institute's system Biblio. For each publication, the authors are obliged to provide both the original abstract in Czech or English, and its translation into English or Czech, respectively. No filtering was performed, except for removing entries missing the Czech or English abstract, and replacing newline and tabulator characters by spaces.
This is a parallel corpus of Czech and mostly English abstracts of scientific papers and presentations published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague. For each publication record, the authors are obliged to provide both the original abstract (in Czech or English), and its translation (English or Czech) in the internal Biblio system. The data was filtered for duplicates and missing entries, ensuring that every record is bilingual. Additionally, records of published papers which are indexed by SemanticScholar contain the respective link. The dataset was created from September 2022 image of the Biblio database and is stored in JSONL format, with each line corresponding to one record.
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving three NLP tasks: machine translation, image captioning, and sentiment analysis.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
Feel free to contact the authors of this submission in case you run into problems!
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving four NLP tasks: machine translation, image captioning, sentiment analysis, and summarization.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
In addition to the models presented in the referenced paper (developed and published in 2018), we include models for automatic news summarization for Czech and English developed in 2019. The Czech models were trained using the SumeCzech dataset (https://www.aclweb.org/anthology/L18-1551.pdf), the English models were trained using the CNN-Daily Mail corpus (https://arxiv.org/pdf/1704.04368.pdf) using the standard recurrent sequence-to-sequence architecture.
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
The summarization models require input that is tokenized with Moses Tokenizer (https://github.com/alvations/sacremoses) and lower-cased.
Feel free to contact the authors of this submission in case you run into problems!
Fairytale Child is a simple chatbot trying to simulate a curious child. It asks the user to tell a fairy tale, often interrupting to ask for details and clarifications. However, it remembers what it was told and tries to show it if possible.
The chatbot can communicate in Czech and in English. It analyzes the morphology of each sentence produced by the user with natural language processing tools, tries to identify potential questions to ask, and then asks one. A morphological generator is employed to generate correctly inflected sentences in Czech, so that the resulting sentences sound as natural as possible.
Pohádkové dítě je jednoduchý chatbot, simulující zvídavé dítě. Požádá uživatele, aby mu vyprávěl pohádku, ale často ho přerušuje, aby se zeptal na detaily a vysvětlení. Pamatuje si ale, co mu uživatel řekl, a snaží se to pokud možno dát najevo.
Chatbot umí komunikovat česky a anglicky. Analyzuje tvarosloví každé uživatelovy věty pomocí NLP nástrojů, pokusí se nalézt chodnou otázku, a tu pak položí. Aby tvořené české věty zněly co nejpřirozeněji, využívá se pro skloňování tvaroslovný generátor. and The work has been supported by GAUK 1572314 and SVV 260104.
It has been using language resources developed, stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2010013).
Fairytale Child is a simple chatbot trying to simulate a curious child. It asks the user to tell a fairy tale, often interrupting to ask for details and clarifications. However, it remembers what it was told and tries to show it if possible.
The chatbot can communicate in Czech and in English. It analyzes the morphology of each sentence produced by the user with natural language processing tools, tries to identify potential questions to ask, and then asks one. A morphological generator is employed to generate correctly inflected sentences in Czech, so that the resulting sentences sound as natural as possible.
Pohádkové dítě je jednoduchý chatbot, simulující zvídavé dítě. Požádá uživatele, aby mu vyprávěl pohádku, ale často ho přerušuje, aby se zeptal na detaily a vysvětlení. Pamatuje si ale, co mu uživatel řekl, a snaží se to pokud možno dát najevo.
Chatbot umí komunikovat česky a anglicky. Analyzuje tvarosloví každé uživatelovy věty pomocí NLP nástrojů, pokusí se nalézt chodnou otázku, a tu pak položí. Aby tvořené české věty zněly co nejpřirozeněji, využívá se pro skloňování tvaroslovný generátor. and The work has been supported by GAUK 1572314 and SVV 260104.
It has been using language resources developed, stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2010013).