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--- |
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license: cc-by-4.0 |
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task_categories: |
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- token-classification |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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tags: |
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- education |
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dataset_info: |
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features: |
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- name: audio_path |
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dtype: string |
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- name: asr_transcript |
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dtype: string |
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- name: original_text |
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dtype: string |
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- name: mutated_text |
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dtype: string |
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- name: index_tags |
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dtype: string |
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- name: mutated_tags |
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dtype: string |
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splits: |
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- name: DEL |
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num_bytes: 208676326 |
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num_examples: 351867 |
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- name: SUB |
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num_bytes: 243003228 |
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num_examples: 351867 |
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- name: REP |
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num_bytes: 303304320 |
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num_examples: 351867 |
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download_size: 317852265 |
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dataset_size: 754983874 |
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--- |
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# Dataset Card for Running Records Errors Dataset |
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## Dataset Description |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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The Running Records Errors dataset is an English-language dataset containing 1,055,601 sentences based on the Europarl corpus. As described in our paper, |
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we take the sentences from the English version of the Europarl corpus and randomly inject three types of errors into the sentences: *repetitions*, where |
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certain words or phrases are repeated, *substitutions*, where certain words are replaced with a different word, and *deletions*, where the word is completely |
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omitted. The sentences are then passed into a TTS pipeline consisting of TacoTron2 and HifiGAN model to produce audio recordings of those mutated sentences. Lastly, |
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the data is passed into a Quartznet 15x5 model which produces a transcript of the spoken audio. |
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### Supported Tasks and Leaderboards |
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The original purpose of this dataset was to construct a model pipeline that could score running records assesments given a transcript of a child's speech along with |
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the true text for that assesment. However, we provide this dataset to support other tasks involving error detection in text. |
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### Languages |
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All of the data in the dataset is in English. |
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## Dataset Structure |
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### Data Instances |
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For each instance, there is a string for the audio transcript, a string for the original text before we added any errors, as well as a string of the sentence with the errors we generated. |
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In addition, we provide two lists. One list denotes the original position of each word in the mutated text, and the second list denotes the error applied to that word. |
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### Data Fields |
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- asr_transcript: The transcript of the audio processed by our Quartznet 15x5 model. |
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- original_text: The original text that was in the Europarl corupus. This text contains no artificial errors. |
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- mutated_text: This text contains the errors we injected. |
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- index_tags: This list denotes the original position of each word in `mutated_text.` |
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- mutated_tags: This list denotes the error applied to each word in `mutated_text.` |
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### Data Splits |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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This dataset was generated with the guidance of @cehrett |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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[More Information Needed] |
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### Contributions |
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[More Information Needed] |
