Phonological networks are representations of word forms and their phonological relationships with other words in a given language lexicon. A principle underlying the growth (or evolution) of those networks is preferential attachment, or the ‘rich-gets-richer’ mechanisms, according to which words with many phonological neighbors (or links) are the main beneficiaries of future growth opportunities. Due to their limited number of words, language lexica constitute node-constrained networks where growth cannot keep increasing in a linear way; hence, preferential attachment is likely mitigated by certain factors. The present study investigated aging effects (i.e., a word’s finite time span of being active in terms of growth) in an evolving phonological network of English as a second language. It was found that phonological neighborhoods are constructed by one large initial lexical spurt, followed by sublinear growth spurts that eventually lead to very limited growth in later lexical spurts during network evolution, all the while obeying the law of preferential attachment. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to attach more distant phonological neighbors in the lower proficiency levels, while phonologically more similar neighbors enter phonological neighborhoods at more advanced levels of English as a second language. Overall, the findings suggest an aging effect in growth that favors younger words. In addition, beginning learners seem to prefer the acquisition of phonological neighbors that are easier to discriminate. Implications for the second language lexicon include leveraged learning mechanisms, learning bouts focussed on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks.
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.
Data
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Bengali Visual Genome (BVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Bengali language. Bengali Visual Genome 1.0 is the multi-modal dataset in Bengali for machine translation and image
captioning. Bengali Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Bengali 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 HGV 1.1 has. For BVG, we manually translated these captions from English to Bengali taking the associated images into account. The manual translation is performed by the native Bengali speakers without referring to any machine translation system.
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. A third test set is
called the ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and
manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
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 - Bengali 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
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The statistics of the current release are given below.
Parallel Corpus Statistics
--------------------------
Dataset Segments English Words Bengali Words
---------- -------- ------------- -------------
Train 28930 143115 113978
Dev 998 4922 3936
Test 1595 7853 6408
Challenge Test 1400 8186 6657
---------- -------- ------------- -------------
Total 32923 164076 130979
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@inproceedings{hindi-visual-genome:2022,
title= "{Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning}",
author={Sen, Arghyadeep
and Parida, Shantipriya
and Kotwal, Ketan
and Panda, Subhadarshi
and Bojar, Ond{\v{r}}ej
and Dash, Satya Ranjan},
editor={Satapathy, Suresh Chandra
and Peer, Peter
and Tang, Jinshan
and Bhateja, Vikrant
and Ghosh, Anumoy},
booktitle= {Intelligent Data Engineering and Analytics},
publisher= {Springer Nature Singapore},
address= {Singapore},
pages = {63--70},
isbn = {978-981-16-6624-7},
doi = {10.1007/978-981-16-6624-7_7},
}
Annotated dataset consisting of personal designations found on websites of 42 German, Austrian, Swiss and South Tyrolean cities. Our goal is to re-evaluate the websites every year in order to see how the use of gender-fair language develops over time. The dataset contains coordinates for the creation of map material.
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}
}
Input data, individual experimental annotations, and a complete and detailed overview of the measured results related to the experiment described in the referenced paper.
Simple question answering database version 3.2 (SQAD v3.2) created from Czech Wikipedia. The new version consists of more than 16000 records. Each record of SQAD consists of multiple files - question, answer extraction, answer selection, URL, question metadata, and in some cases, answer context.