PIE Dataset Card for "aae2"

This is a PyTorch-IE wrapper for the Argument Annotated Essays v2 (AAE2) dataset (paper and homepage). Since the AAE2 dataset is published in the BRAT standoff format, this dataset builder is based on the PyTorch-IE brat dataset loading script.

Therefore, the aae2 dataset as described here follows the data structure from the PIE brat dataset card.

Usage

from pie_datasets import load_dataset
from pie_datasets.builders.brat import BratDocumentWithMergedSpans
from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions

# load default version
dataset = load_dataset("pie/aae2")
assert isinstance(dataset["train"][0], BratDocumentWithMergedSpans)

# if required, normalize the document type (see section Document Converters below)
dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions)
assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions)

# get first relation in the first document
doc = dataset_converted["train"][0]
print(doc.binary_relations[0])
# BinaryRelation(head=LabeledSpan(start=716, end=851, label='Premise', score=1.0), tail=LabeledSpan(start=591, end=714, label='Claim', score=1.0), label='supports', score=1.0)
print(doc.binary_relations[0].resolve())
# ('supports', (('Premise', 'What we acquired from team work is not only how to achieve the same goal with others but more importantly, how to get along with others'), ('Claim', 'through cooperation, children can learn about interpersonal skills which are significant in the future life of all students')))

Dataset Summary

Argument Annotated Essays Corpus (AAEC) (Stab and Gurevych, 2017) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, which can stand in isolation as a complete sentence, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called "shell" language such as On the other hand and Hence. (Morio et al., 2022, p. 642)

In the original dataset, there is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16). However, through our DOCUMENT_CONVERTERS, we build links between claims, creating a graph structure throughout an entire essay (see Document Converters).

Supported Tasks and Leaderboards

• Tasks: Argumentation Mining, Component Identification, Component Classification, Structure Identification
• Leaderboard: More Information Needed

Languages

The language in the dataset is English (persuasive essays).

Dataset Variants

The aae2 dataset comes in a single version (default) with BratDocumentWithMergedSpans as document type. Note, that this in contrast to the base brat dataset, where the document type for the default variant is BratDocument. The reason is that the AAE2 dataset has already been published with only single-fragment spans. Without any need to merge fragments, the document type BratDocumentWithMergedSpans is easier to handle for most of the task modules.

Data Schema

See PIE-Brat Data Schema.

Data Splits

Statistics	Train	Test
No. of document	322	80
Components - MajorClaim - Claim - Premise	598 1202 3023	153 304 809
Relations* - supports - attacks	3820 405	1021 92

* included all relations between claims and premises and all claim attributions.

See further statistics in Stab & Gurevych (2017), p. 650, Table A.1.

Label Descriptions and Statistics

Components

Components	Count	Percentage
MajorClaim	751	12.3 %
Claim	1506	24.7 %
Premise	3832	62.9 %

• MajorClaim is the root node of the argumentation structure and represents the author’s standpoint on the topic. Essay bodies either support or attack the author’s standpoint expressed in the major claim. The major claim can be mentioned multiple times in a single document.
• Claim constitutes the central component of each argument. Each one has at least one premise and takes stance attribute values "for" or "against" with regarding the major claim.
• Premise is the reasons of the argument; either linked to claim or another premise.

Relations

Relations	Count	Percentage
support: supports	3613	94.3 %
attack: attacks	219	5.7 %

• "Each premise p has one outgoing relation (i.e., there is a relation that has p as source component) and none or several incoming relations (i.e., there can be a relation with p as target component)."
• "A Claim can exhibit several incoming relations but no outgoing relation." (S&G, 2017, p. 68)
• "The relations from the claims of the arguments to the major claim are dotted since we will not explicitly annotated them. The relation of each argument to the major claim is indicated by a stance attribute of each claim. This attribute can either be for or against as illustrated in figure 1.4." (Stab & Gurevych, Guidelines for Annotating Argumentation Structures in Persuasive Essays, 2015, p. 5)

See further description in Stab & Gurevych 2017, p.627 and the annotation guideline.

Note that relations between MajorClaim and Claim were not annotated; however, each claim is annotated with an Attribute annotation with value for or against - which indicates the relation between itself and MajorClaim. In addition, when two non-related Claim 's appear in one paragraph, there is also no relations to one another. An example of a document is shown here below.

Example

Document Converters

The dataset provides document converters for the following target document types:

• pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations with layers:
  • labeled_spans: LabeledSpan annotations, converted from BratDocumentWithMergedSpans's spans
    • labels: MajorClaim, Claim, Premise
  • binary_relations: BinaryRelation annotations, converted from BratDocumentWithMergedSpans's relations
    • there are two conversion methods that convert Claim attributes to their relations to MajorClaim (also see the label-count changes after this relation conversion here below):
      • connect_first (default setting):
        • build a supports or attacks relation from each Claim to the first MajorClaim depending on the Claim's attribute (for or against), and
        • build a semantically_same relation between following MajorClaim to the first MajorClaim
      • connect_all
        • build a supports or attacks relation from each Claim to every MajorClaim
        • no relations between each MajorClaim
    • labels: supports, attacks, and semantically_same if connect_first
• pytorch_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions with layers:
  • labeled_spans, as above
  • binary_relations, as above
  • labeled_partitions, LabeledSpan annotations, created from splitting BratDocumentWithMergedSpans's text at new lines (\n).
    • every partition is labeled as paragraph

See here for the document type definitions.

Relation Label Statistics after Document Conversion

When converting from BratDocumentWithMergedSpan to TextDocumentWithLabeledSpansAndBinaryRelations and TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions, we apply a relation-conversion method (see above) that changes the label counts for the relations, as follows:

1. connect_first (default):

Relations	Count	Percentage
support: supports	4841	85.1 %
attack: attacks	497	8.7 %
other: semantically_same	349	6.2 %

2. connect_all

Relations	Count	Percentage
support: supports	5958	89.3 %
attack: attacks	715	10.7 %

Collected Statistics after Document Conversion

We use the script evaluate_documents.py from PyTorch-IE-Hydra-Template to generate these statistics. After checking out that code, the statistics and plots can be generated by the command:

python src/evaluate_documents.py dataset=aae2_base metric=METRIC

where a METRIC is called according to the available metric configs in config/metric/METRIC (see metrics).

This also requires to have the following dataset config in configs/dataset/aae2_base.yaml of this dataset within the repo directory:

_target_: src.utils.execute_pipeline
input:
  _target_: pie_datasets.DatasetDict.load_dataset
  path: pie/aae2
  revision: 1015ee38bd8a36549b344008f7a49af72956a7fe

For token based metrics, this uses bert-base-uncased from transformer.AutoTokenizer (see AutoTokenizer, and bert-based-uncased to tokenize text in TextDocumentWithLabeledSpansAndBinaryRelations (see document type).

For relation-label statistics, we collect those from the default relation conversion method, i.e., connect_first, resulting in three distinct relation labels.

Relation argument (outer) token distance per label

The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance.

We collect the following statistics: number of documents in the split (no. doc), no. of relations (len), mean of token distance (mean), standard deviation of the distance (std), minimum outer distance (min), and maximum outer distance (max). We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.

Command

python src/evaluate_documents.py dataset=aae2_base metric=relation_argument_token_distances

train (322 documents)

	len	max	mean	min	std
ALL	9002	514	102.582	9	93.76
attacks	810	442	127.622	10	109.283
semantically_same	552	514	301.638	25	73.756
supports	7640	493	85.545	9	74.023

Histogram (split: train, 322 documents)

test (80 documents)

	len	max	mean	min	std
ALL	2372	442	100.711	10	92.698
attacks	184	402	115.891	12	98.751
semantically_same	146	442	299.671	34	72.921
supports	2042	437	85.118	10	75.023

Histogram (split: test, 80 documents)

Span lengths (tokens)

The span length is measured from the first token of the first argumentative unit to the last token of the particular unit.

We collect the following statistics: number of documents in the split (no. doc), no. of spans (len), mean of number of tokens in a span (mean), standard deviation of the number of tokens (std), minimum tokens in a span (min), and maximum tokens in a span (max). We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.

Command

python src/evaluate_documents.py dataset=aae2_base metric=span_lengths_tokens

statistics	train	test
no. doc	322	80
len	4823	1266
mean	17.157	16.317
std	8.079	7.953
min	3	3
max	75	50

Histogram (split: train, 332 documents)

Histogram (split: test, 80 documents)

Token length (tokens)

The token length is measured from the first token of the document to the last one.

We collect the following statistics: number of documents in the split (no. doc), mean of document token-length (mean), standard deviation of the length (std), minimum number of tokens in a document (min), and maximum number of tokens in a document (max). We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.

Command

python src/evaluate_documents.py dataset=aae2_base metric=count_text_tokens

statistics	train	test
no. doc	322	80
mean	377.686	378.4
std	64.534	66.054
min	236	269
max	580	532

Histogram (split: train, 332 documents)

Histogram (split: test, 80 documents)

Dataset Creation

Curation Rationale

"The identification of argumentation structures involves several subtasks like separating argumentative from non-argumentative text units (Moens et al. 2007; Florou et al. 2013), classifying argument components into claims and premises (Mochales-Palau and Moens 2011; Rooney, Wang, and Browne 2012; Stab and Gurevych 2014b), and identifying argumentative relations (Mochales-Palau and Moens 2009; Peldszus 2014; Stab and Gurevych 2014b). However, an approach that covers all subtasks is still missing. However, an approach that covers all subtasks is still missing. Furthermore, most approaches operate locally and do not optimize the global argumentation structure.

"In addition, to the lack of end-to-end approaches for parsing argumentation structures, there are relatively few corpora annotated with argumentation structures at the discourse-level." (p. 621)

"Our primary motivation for this work is to create argument analysis methods for argumentative writing support systems and to achieve a better understanding of argumentation structures." (p. 622)

Source Data

Persuasive essays were collected from essayforum.com (See essay prompts, along with the essay's id's here).

Initial Data Collection and Normalization

"We randomly selected 402 English essays with a description of the writing prompt from essayforum.com. This online forum is an active community that provides correction and feedback about different texts such as research papers, essays, or poetry. For example, students post their essays in order to receive feedback about their writing skills while preparing for standardized language tests. The corpus includes 7,116 sentences with 147,271 tokens." (p. 630)

Who are the source language producers?

More Information Needed

Annotations

Annotation process

The annotation were done using BRAT Rapid Annotation Tool (Stenetorp et al., 2012).

All three annotators independently annotated a random subset of 80 essays. The remaining 322 essays were annotated by the expert annotator.

The authors evaluated the inter-annotator agreement using observed agreement and Fleiss’ κ (Fleiss 1971), on each label on each sub-tasks, namely, component identification, component classification, and relation identification. The results were reported in their paper in Tables 2-4.

Who are the annotators?

Three non-native speakers; one of the three being an expert annotator.

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

"[Computational Argumentation] have broad application potential in various areas such as legal decision support (Mochales-Palau and Moens 2009), information retrieval (Carstens and Toni 2015), policy making (Sardianos et al. 2015), and debating technologies (Levy et al. 2014; Rinott et al. 2015)." (p. 619)

Discussion of Biases

More Information Needed

Other Known Limitations

The relations between claims and major claims are not explicitly annotated.

"The proportion of non-argumentative text amounts to 47,474 tokens (32.2%) and 1,631 sentences (22.9%). The number of sentences with several argument components is 583, of which 302 include several components with different types (e.g., a claim followed by premise)... [T]he identification of argument components requires the separation of argumentative from non-argumentative text units and the recognition of component boundaries at the token level...The proportion of paragraphs with unlinked argument components (e.g., unsupported claims without incoming relations) is 421 (23%). Thus, methods that link all argument components in a paragraph are only of limited use for identifying the argumentation structures in our corpus.

"Most of the arguments are convergent—that is, the depth of the argument is 1. The number of arguments with serial structure is 236 (20.9%)." (p. 634)

Additional Information

Dataset Curators

More Information Needed

Licensing Information

License: License description by TU Darmstadt

Funding: This work has been supported by the Volkswagen Foundation as part of the Lichtenberg-Professorship Program under grant no. I/82806 and by the German Federal Ministry of Education and Research (BMBF) as a part of the Software Campus project AWS under grant no. 01—S12054.

Citation Information

@article{stab2017parsing,
  title={Parsing argumentation structures in persuasive essays},
  author={Stab, Christian and Gurevych, Iryna},
  journal={Computational Linguistics},
  volume={43},
  number={3},
  pages={619--659},
  year={2017},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@misc{https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2422,
url = { https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2422 },
author = { Stab, Christian and Gurevych, Iryna },
keywords = { Argument Mining, 409-06 Informationssysteme, Prozess- und Wissensmanagement, 004 },
publisher = { Technical University of Darmstadt },
year = { 2017 },
copyright = { License description },
title = { Argument Annotated Essays (version 2) }
}

Contributions

Thanks to @ArneBinder and @idalr for adding this dataset.