Datasets:
mocha

	---
	pretty_name: MOCHA
	annotations_creators:
	- crowdsourced
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-sa-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- question-answering
	task_ids: []
	paperswithcode_id: mocha
	tags:
	- generative-reading-comprehension-metric
	dataset_info:
	features:
	- name: constituent_dataset
	dtype: string
	- name: id
	dtype: string
	- name: context
	dtype: string
	- name: question
	dtype: string
	- name: reference
	dtype: string
	- name: candidate
	dtype: string
	- name: score
	dtype: float32
	- name: metadata
	struct:
	- name: scores
	sequence: int32
	- name: source
	dtype: string
	- name: candidate2
	dtype: string
	- name: score2
	dtype: float32
	splits:
	- name: train
	num_bytes: 33292592
	num_examples: 31069
	- name: validation
	num_bytes: 4236883
	num_examples: 4009
	- name: test
	num_bytes: 6767409
	num_examples: 6321
	- name: minimal_pairs
	num_bytes: 193560
	num_examples: 200
	download_size: 14452311
	dataset_size: 44490444
	---

	# Dataset Card for Mocha

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Mocha](https://allennlp.org/mocha)
	- Repository: [https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA)
	- Paper: [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/)
	- Leaderboard:
	- Point of Contact:

	### Dataset Summary

	Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics.

	### Supported Tasks and Leaderboards

	[More Information Needed]

	### Languages

	English

	## Dataset Structure

	### Data Instances

	MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores.

	### Data Fields

	- `constituent_dataset`: the original QA dataset which the data instance came from.
	- `id`
	- `context`: the passage content.
	- `question`: the question related to the passage content.
	- `reference`: the correct answer for the question.
	- `candidate`: the answer generated from the `reference` by `source`
	- `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1`
	- `metadata`: Not included in minimal pairs split.
	- `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]`
	- `source`: the generative model to generate the `candidate`

	In minimal pairs, we'll have an additional candidate for robust evaluation.

	- `candidate2`
	- `score2`

	### Data Splits

	Dataset Split \| Number of Instances in Split
	--------------\|--------------------------------------------
	Train \| 31,069
	Validation \| 4,009
	Test \| 6,321
	Minimal Pairs \| 200

	## Dataset Creation

	### Curation Rationale

	[More Information Needed]

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)

	### Citation Information

	```bitex
	@inproceedings{Chen2020MOCHAAD,
	author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
	title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
	booktitle={EMNLP},
	year={2020}
	}
	```

	### Contributions

	Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.