Update README.md

README.md

@@ -14,26 +14,10 @@ pretty_name: HL (High-Level Dataset)
 size_categories:
 - 10K<n<100K
 annotations_creators:
-- crowdsurced
+- crowdsourced
 annotations_origin:
-- crowdsurced
+- crowdsourced
 dataset_info:
-  features:
-    - name: file_name
-      dtype: string
-      captions:
-      - name: scene
-        sequence:
-          dtype: string
-      - name: action
-        sequence:
-          dtype: string
-      - name: rationale
-        sequence:
-          dtype: string
-      - name: object
-        sequence:
-          dtype: string
   splits:
     - name: train
       num_examples: 13498
@@ -45,7 +29,6 @@ dataset_info:
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
   - [Supported Tasks](#supported-tasks)
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
@@ -69,73 +52,187 @@ dataset_info:
 
 ## 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 149997 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 are the human interpretation of the images thus, they look more natural.
+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 can is close to commonsense (in a Likert scale from 1-5).
+
+
 - **Homepage:**
-- **Repository:
+- **Repository:**
 - **Paper:**
 - **Point of Contact:**
   
-### Dataset Summary
-
-[More Information Needed]
 
 ### Supported Tasks
 
+- image captioning
+- visual question answering
+- multimodal text-scoring
+- zero-shot evaluation
+
 ### Languages
 
 English
 
 ## Dataset Structure
 
-[More Information Needed]
+The dataset is provided with images from COCO and two metadata jsonl files containing the annotations
 
 ### Data Instances
 
-[More Information Needed]
+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
 
-[More Information Needed]
+- ```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
 
-[More Information Needed]
+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
 
-### Curation Rationale
+The dataset has been crowdsourced on Amazon Mechanical Turk.
+From the paper:
 
-[More Information Needed]
+>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.
 
-### Source Data
+### Curation Rationale
 
-[More Information Needed]
+From the paper:
 
-#### Initial Data Collection and Normalization
+>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.
 
-[More Information Needed]
 
-#### Who are the source language producers?
+### Source Data
 
-[More Information Needed]
+- Images:  COCO
+- object axis annotations: COCO
+- scene, action, rationale annotations: crowdsourced
+- confidence scores: crowdsourced
+- purity score and diversity score: automatically computed
 
-### Annotations
+#### Annotation process
 
-[More Information Needed]
+From the paper:
 
-#### Annotation process
+>**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.
 
-[More Information Needed]
 
 #### Who are the annotators?
 
-[More Information Needed]
+Turkers from Amazon Mechanical Turk
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+There is no personal or sensitive information
 
 ## Considerations for Using the Data
 
+[More Information Needed]
+
 ### Social Impact of Dataset
 
 [More Information Needed]
@@ -146,17 +243,30 @@ English
 
 ### Other Known Limitations
 
-[More Information Needed]
+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
 
-## Additional Information
+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
 
-[More Information Needed]
+Michele Cafagna
 
 ### Licensing Information
 
-[More Information Needed]
+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