add dataset card

README.md

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+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en-US
+licenses: []
+multilinguality:
+- monolingual
+pretty_name: Scientific Dependency Tree Bank
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- parsing
+
+# Dataset Card for SciDTB
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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
+
+- **Homepage:** https://github.com/PKU-TANGENT/SciDTB
+- **Repository:** https://github.com/PKU-TANGENT/SciDTB
+- **Paper:** https://aclanthology.org/P18-2071/
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+SciDTB is a domain-specific discourse treebank annotated on scientific articles written in English-language. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. Furthermore, this treebank is made as a benchmark for evaluating discourse dependency parsers. This dataset can benefit many downstream NLP tasks such as machine translation and automatic summarization. 
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point consist of `root` which is a list of nodes in dependency tree. Each node in the list has four fields: `id` containing id for the node, `parent` contains id of the parent node, `text` refers to the span that is part of the current node and finally `relation` represents relation between current node and parent node.
+
+An example from SciDTB train set is given below:
+{
+	"root": [
+		{
+			"id": 0,
+			"parent": -1,
+			"text": "ROOT",
+			"relation": "null"
+		},
+		{
+			"id": 1,
+			"parent": 0,
+			"text": "We propose a neural network approach ",
+			"relation": "ROOT"
+		},
+		{
+			"id": 2,
+			"parent": 1,
+			"text": "to benefit from the non-linearity of corpus-wide statistics for part-of-speech ( POS ) tagging . <S>",
+			"relation": "enablement"
+		},
+		{
+			"id": 3,
+			"parent": 1,
+			"text": "We investigated several types of corpus-wide information for the words , such as word embeddings and POS tag distributions . <S>",
+			"relation": "elab-aspect"
+		},
+		{
+			"id": 4,
+			"parent": 5,
+			"text": "Since these statistics are encoded as dense continuous features , ",
+			"relation": "cause"
+		},
+		{
+			"id": 5,
+			"parent": 3,
+			"text": "it is not trivial to combine these features ",
+			"relation": "elab-addition"
+		},
+		{
+			"id": 6,
+			"parent": 5,
+			"text": "comparing with sparse discrete features . <S>",
+			"relation": "comparison"
+		},
+		{
+			"id": 7,
+			"parent": 1,
+			"text": "Our tagger is designed as a combination of a linear model for discrete features and a feed-forward neural network ",
+			"relation": "elab-aspect"
+		},
+		{
+			"id": 8,
+			"parent": 7,
+			"text": "that captures the non-linear interactions among the continuous features . <S>",
+			"relation": "elab-addition"
+		},
+		{
+			"id": 9,
+			"parent": 10,
+			"text": "By using several recent advances in the activation functions for neural networks , ",
+			"relation": "manner-means"
+		},
+		{
+			"id": 10,
+			"parent": 1,
+			"text": "the proposed method marks new state-of-the-art accuracies for English POS tagging tasks . <S>",
+			"relation": "evaluation"
+		}
+	]
+}
+
+More such raw data instance can be found [here](https://github.com/PKU-TANGENT/SciDTB/tree/master/dataset)
+
+### Data Fields
+
+- id: an integer identifier for the node
+- parent: an integer identifier for the parent node
+- text: a string containing text for the current node
+- relation: a string representing discourse relation between current node and parent node
+
+### Data Splits
+
+Dataset consists of three splits: `train`, `dev` and  `test`. 
+Tain Dev Test 743 154 152
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+[Needs More Information]