ESIC (Europarl Simultaneous Interpreting Corpus) is a corpus of 370 speeches (10 hours) in English, with manual transcripts, transcribed simultaneous interpreting into Czech and German, and parallel translations.
The corpus contains source English videos and audios. The interpreters' voices are not published within the corpus, but there is a tool that downloads them from the web of European Parliament, where they are publicly avaiable.
The transcripts are equipped with metadata (disfluencies, mixing voices and languages, read or spontaneous speech, etc.), punctuated, and with word-level timestamps.
The speeches in the corpus come from the European Parliament plenary sessions, from the period 2008-11. Most of the speakers are MEP, both native and non-native speakers of English. The corpus contains metadata about the speakers (name, surname, id, fraction) and about the speech (date, topic, read or spontaneous).
ESIC has validation and evaluation parts.
The current version is ESIC v1.1, it extends v1.0 with manual sentence alignment of the tri-parallel texts, and with bi-parallel sentence alignment of English original transcripts and German interpreting.
POS Tagger and Lemmatizer models for EvaLatin2020 data (https://github.com/CIRCSE/LT4HALA). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#evalatin20_models .
To use these models, you need UDPipe version at least 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .
EVALD 4.0 for Beginners is a software that serves for automatic evaluation of Czech texts written by non-native speakers of Czech – language beginners.
EVALD 4.0 for Foreigners is a software for automatic evaluation of surface coherence (cohesion) in Czech texts written by non-native speakers of Czech.
This machine translation test set contains 2223 Czech sentences collected within the FAUST project (https://ufal.mff.cuni.cz/grants/faust, http://hdl.handle.net/11234/1-3308).
Each original (noisy) sentence was normalized (clean1 and clean2) and translated to English independently by two translators.
ForFun is a database of linguistic forms and their syntactic functions built with the use of the multi-layer annotated corpora of Czech, the Prague Dependency Treebanks. The purpose of the Prague Database of Forms and Functions (ForFun) is to help the linguists to study the form-function relation, which we assume to be one of the principal tasks of both theoretical linguistics and natural language processing.
A prototypical question to be asked is "What purposes does a preposition 'po' serve for" or "What are the linguistic means in the sentence that can express the meaning 'a destination of an action'?". There are almost 1500 distinct forms (besides the 'po' preposition) and 65 distinct functions (besides the 'destination').
The dataset of handwritten Czech text lines, sourced from two chronicles (municipal chronicles 1931-1944, school chronicles 1913-1933).
The dataset comprises 25k lines machine-extracted from scanned pages, and provides manual annotation of text contents for a subset of size 2k.
Data
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Hausa Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hausa multimodal machine translation tasks and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as the dataset Hindi Visual Genome 1.1 has. We automatically translated the English captions to Hausa and manually post-edited, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments is available for the multi-modal task. This challenge test set was created in Hindi Visual Genome by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
-----------------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hausa Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width, and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
--------------------
The statistics of the current release are given below.
Parallel Corpus Statistics
-----------------------------------
Dataset Segments English Words Hausa Words
---------- -------- ------------- -----------
Train 28930 143106 140981
Dev 998 4922 4857
Test 1595 7853 7736
Challenge Test 1400 8186 8752
---------- -------- ------------- -----------
Total 32923 164067 162326
The word counts are approximate, prior to tokenization.
Citation
-----------
If you use this corpus, please cite the following paper:
@InProceedings{abdulmumin-EtAl:2022:LREC,
author = {Abdulmumin, Idris
and Dash, Satya Ranjan
and Dawud, Musa Abdullahi
and Parida, Shantipriya
and Muhammad, Shamsuddeen
and Ahmad, Ibrahim Sa'id
and Panda, Subhadarshi
and Bojar, Ond{\v{r}}ej
and Galadanci, Bashir Shehu
and Bello, Bello Shehu},
title = "{Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation}",
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {6471--6479},
url = {https://aclanthology.org/2022.lrec-1.694}
}
Data
----
Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
}