Added more details about the dataset

README.md

@@ -36,4 +36,72 @@ tags:
 
 ### Dataset Summary
 
-VoxDIY RusNews is a dataset for crowdsourced text aggregation collected via VoxDIY pipeline. RusNews dataset was used as a source for the speech generation step.
+VoxDIY RusNews is the first publicly available large-scale dataset of crowdsourced audio transcriptions in Russian language.
+The dataset was constructed by annotating audio recordings of Russian sentences from news domain on [Toloka crowdsourcing platform](https://toloka.ai).
+VoxDIY RusNews consists of 3091 instances having around 21K annotations obtained from crowd workers.
+
+### Supported Tasks and Leaderboards
+Aggregation of crowd transcriptions.
+
+### Languages
+Russian
+
+## Dataset Structure
+
+### Data Instances
+
+A data instance contains a url to the audio recording, a list of transcriptions along with the corresponding performers identifiers and 
+ground truth. For each data instance, seven crowdsourced transcriptions are provided.
+
+```
+{'task': 'https://tlk.s3.yandex.net/annotation_tasks/russian/1003.wav',
+ 'transcriptions': 'в список так же попали мэрлин монро джон ленон и альберт эйнштейн | в список также попали мерлин монро джон ленон и альберт энштейн | в список также попали мерилин монро джон леннон и альберт энтштейн | в список также попали мэрилин монро джон леннон и альберт эпштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн | в список так же попали мерелин монро джон ленон и альберт нштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн',
+ 'performers': '1743 | 784 | 1014 | 1572 | 744 | 2187 | 1208',
+ 'gt': 'в список также попали мэрилин монро джон леннон и альберт эйнштейн'}
+```
+
+### Data Fields
+
+* task: a string containing a url of the audio recording
+* transcriptions: a list of the crowdsourced transcriptions separated by '|'
+* performers: the corresponding performers' identifiers.
+* gt: ground truth transcription
+
+## Dataset Creation
+
+### Source Data
+
+The audio recordings were obtained using a [speech synthesis tool](https://cloud.yandex.com/en-ru/services/speechkit).
+The source sentences come from the Russian test set of the machine translation shared task executed as a part of the 
+Eights and Ninth Workshops on Statistical Machine Translation ([WMT 2013](https://www.statmt.org/wmt13/) and [WMT 2014](https://www.statmt.org/wmt14/)).
+
+### Annotations
+
+Annotation was done on [Toloka crowdsourcing platform](https://toloka.ai) with overlap of 7 (that is, each task was performed by 7 annotators).
+
+Only annotators who self-reported the knowledge of Russian had access to the annotation task.
+Additionally, annotators had to pass *Entrance Exam*. For this, we ask all incoming eligible workers to annotate ten audio
+recordings. We then compute our target metric — Word Error Rate (WER) — on these recordings and accept to the main task all workers 
+who achieve WER of 40% or less (the smaller the value of the metric, the higher the quality of annotation).
+
+The Toloka crowdsourcing platform associates workers with unique identifiers and returns these identifiers to the requester. 
+To further protect the data, we additionally encode each identifier with an integer that is eventually reported in our released datasets.
+
+See more details in the [paper](https://arxiv.org/pdf/2107.01091.pdf).
+
+### Citation Information
+
+```
+@inproceedings{CrowdSpeech,
+  author    = {Pavlichenko, Nikita and Stelmakh, Ivan and Ustalov, Dmitry},
+  title     = {{CrowdSpeech and Vox~DIY: Benchmark Dataset for Crowdsourced Audio Transcription}},
+  year      = {2021},
+  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
+  eprint    = {2107.01091},
+  eprinttype = {arxiv},
+  eprintclass = {cs.SD},
+  url       = {https://openreview.net/forum?id=3_hgF1NAXU7},
+  language  = {english},
+  pubstate  = {forthcoming},
+}
+```