Datasets:

michelecafagna26
/

hl

	---
	license: apache-2.0
	task_categories:
	- image-to-text
	- question-answering
	- zero-shot-classification
	language:
	- en
	multilinguality:
	- monolingual
	task_ids:
	- text-scoring
	pretty_name: HL (High-Level Dataset)
	size_categories:
	- 10K<n<100K
	annotations_creators:
	- crowdsourced
	annotations_origin:
	- crowdsourced
	dataset_info:
	splits:
	- name: train
	num_examples: 13498
	- name: test
	num_examples: 1499
	---
	# Dataset Card for the High-Level Dataset

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Supported Tasks](#supported-tasks)
	- [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)

	## Dataset Description

	The High-Level (HL) dataset aligns object-centric descriptions from [COCO](https://arxiv.org/pdf/1405.0312.pdf)
	with high-level descriptions crowdsourced along 3 axes: _scene_, _action_, _rationale_

	The HL dataset contains 14997 images from COCO and a total of 134973 crowdsourced captions (3 captions for each axis) aligned with ~749984 object-centric captions from COCO.

	Each axis is collected by asking the following 3 questions:

	1) Where is the picture taken?
	2) What is the subject doing?
	3) Why is the subject doing it?

	The high-level descriptions capture the human interpretations of the images. These interpretations contain abstract concepts not directly linked to physical objects.
	Each high-level description is provided with a _confidence score_, crowdsourced by an independent worker measuring the extent to which
	the high-level description is likely given the corresponding image, question, and caption. The higher the score, the more the high-level caption is close to the commonsense (in a Likert scale from 1-5).

	- 🗃️ Repository: [github.com/michelecafagna26/HL-dataset](https://github.com/michelecafagna26/HL-dataset)
	- 📜 Paper: [HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales](https://arxiv.org/abs/2302.12189?context=cs.CL)
	- 🧭 Spaces: [Dataset explorer](https://huggingface.co/spaces/michelecafagna26/High-Level-Dataset-explorer)
	- 🖊️ Contact: michele.cafagna@um.edu.mt

	### Supported Tasks

	- image captioning
	- visual question answering
	- multimodal text-scoring
	- zero-shot evaluation

	### Languages

	English

	## Dataset Structure

	The dataset is provided with images from COCO and two metadata jsonl files containing the annotations

	### Data Instances

	An instance looks like this:
	```json
	{
	"file_name": "COCO_train2014_000000138878.jpg",
	"captions": {
	"scene": [
	"in a car",
	"the picture is taken in a car",
	"in an office."
	],
	"action": [
	"posing for a photo",
	"the person is posing for a photo",
	"he's sitting in an armchair."
	],
	"rationale": [
	"to have a picture of himself",
	"he wants to share it with his friends",
	"he's working and took a professional photo."
	],
	"object": [
	"A man sitting in a car while wearing a shirt and tie.",
	"A man in a car wearing a dress shirt and tie.",
	"a man in glasses is wearing a tie",
	"Man sitting in the car seat with button up and tie",
	"A man in glasses and a tie is near a window."
	]
	},
	"confidence": {
	"scene": [
	5,
	5,
	4
	],
	"action": [
	5,
	5,
	4
	],
	"rationale": [
	5,
	5,
	4
	]
	},
	"purity": {
	"scene": [
	-1.1760284900665283,
	-1.0889461040496826,
	-1.442818284034729
	],
	"action": [
	-1.0115827322006226,
	-0.5917857885360718,
	-1.6931917667388916
	],
	"rationale": [
	-1.0546956062316895,
	-0.9740906357765198,
	-1.2204363346099854
	]
	},
	"diversity": {
	"scene": 25.965358893403383,
	"action": 32.713305568898775,
	"rationale": 2.658757840479801
	}
	}
	```

	### Data Fields

	- ```file_name```: original COCO filename
	- ```captions```: Dict containing all the captions for the image. Each axis can be accessed with the axis name and it contains a list of captions.
	- ```confidence```: Dict containing the captions confidence scores. Each axis can be accessed with the axis name and it contains a list of captions. Confidence scores are not provided for the _object_ axis (COCO captions).t
	- ```purity score```: Dict containing the captions purity scores. The purity score measures the semantic similarity of the captions within the same axis (Bleurt-based).
	- ```diversity score```: Dict containing the captions diversity scores. The diversity score measures the lexical diversity of the captions within the same axis (Self-BLEU-based).

	### Data Splits

	There are 14997 images and 134973 high-level captions split into:
	- Train-val: 13498 images and 121482 high-level captions
	- Test: 1499 images and 13491 high-level captions

	## Dataset Creation

	The dataset has been crowdsourced on Amazon Mechanical Turk.
	From the paper:

	>We randomly select 14997 images from the COCO 2014 train-val split. In order to answer questions related to _actions_ and _rationales_ we need to
	> ensure the presence of a subject in the image. Therefore, we leverage the entity annotation provided in COCO to select images containing
	> at least one person. The whole annotation is conducted on Amazon Mechanical Turk (AMT). We split the workload into batches in order to ease
	>the monitoring of the quality of the data collected. Each image is annotated by three different annotators, therefore we collect three annotations per axis.

	### Curation Rationale

	From the paper:

	>In this work, we tackle the issue of grounding high-level linguistic concepts in the visual modality, proposing the High-Level (HL) Dataset: a
	V\&L resource aligning existing object-centric captions with human-collected high-level descriptions of images along three different axes: _scenes_, _actions_ and _rationales_.
	The high-level captions capture the human interpretation of the scene, providing abstract linguistic concepts complementary to object-centric captions
	>used in current V\&L datasets, e.g. in COCO. We take a step further, and we collect _confidence scores_ to distinguish commonsense assumptions
	>from subjective interpretations and we characterize our data under a variety of semantic and lexical aspects.


	### Source Data

	- Images: COCO
	- object axis annotations: COCO
	- scene, action, rationale annotations: crowdsourced
	- confidence scores: crowdsourced
	- purity score and diversity score: automatically computed

	#### Annotation process

	From the paper:

	>Pilot: We run a pilot study with the double goal of collecting feedback and defining the task instructions.
	>With the results from the pilot we design a beta version of the task and we run a small batch of cases on the crowd-sourcing platform.
	>We manually inspect the results and we further refine the instructions and the formulation of the task before finally proceeding with the
	>annotation in bulk. The final annotation form is shown in Appendix D.

	>*Procedure:* The participants are shown an image and three questions regarding three aspects or axes: _scene_, _actions_ and _rationales_
	> i,e. _Where is the picture taken?_, _What is the subject doing?_, _Why is the subject doing it?_. We explicitly ask the participants to use
	>their personal interpretation of the scene and add examples and suggestions in the instructions to further guide the annotators. Moreover,
	>differently from other VQA datasets like (Antol et al., 2015) and (Zhu et al., 2016), where each question can refer to different entities
	>in the image, we systematically ask the same three questions about the same subject for each image. The full instructions are reported
	>in Figure 1. For details regarding the annotation costs see Appendix A.


	#### Who are the annotators?

	Turkers from Amazon Mechanical Turk

	### Personal and Sensitive Information

	There is no personal or sensitive information

	## Considerations for Using the Data

	[More Information Needed]

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	From the paper:

	>Quantitying grammatical errors: We ask two expert annotators to correct grammatical errors in a sample of 9900 captions, 900 of which are shared between the two annotators.
	> The annotators are shown the image caption pairs and they are asked to edit the caption whenever they identify a grammatical error.
	>The most common errors reported by the annotators are:
	>- Misuse of prepositions
	>- Wrong verb conjugation
	>- Pronoun omissions

	>In order to quantify the extent to which the corrected captions differ from the original ones, we compute the Levenshtein distance (Levenshtein, 1966) between them.
	>We observe that 22.5\% of the sample has been edited and only 5\% with a Levenshtein distance greater than 10. This suggests a reasonable
	>level of grammatical quality overall, with no substantial grammatical problems. This can also be observed from the Levenshtein distance
	>distribution reported in Figure 2. Moreover, the human evaluation is quite reliable as we observe a moderate inter-annotator agreement
	>(alpha = 0.507, (Krippendorff, 2018) computed over the shared sample.

	### Dataset Curators

	Michele Cafagna

	### Licensing Information

	The Images and the object-centric captions follow the [COCO terms of Use](https://cocodataset.org/#termsofuse)
	The remaining annotations are licensed under Apache-2.0 license.

	### Citation Information

	```BibTeX
	@inproceedings{cafagna2023hl,
	title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and
	{R}ationales},
	author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert},
	booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)},
	address = {Prague, Czech Republic},
	year={2023}
	}
	```