Argument Aspect Corpus - Nuclear Energy

The Argument Aspect Corpus–Nuclear Energy (AAC-NE) contains English-language sentences with aspect annotations describing the content of arguments on the topic of nuclear energy.

It was introduced in this paper:

Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA).

The AAC-NE corpus is based on a subset of all argumentative sentences contained in the UKP SAM dataset [1] for which a majority vote of three annotators could be achieved during the annotation of the main argument aspect of each sentence.

The CSV files contain one of nine aspect labels per argumentative sentence split into training, dev, and test set.

aspect
train
dev
test
Sum
Kripp. Alpha

alternatives
100
16
21
137
0.69

costs
98
17
29
144
0.72

environment
209
27
64
300
0.74

innovation
33
2
8
43
0.38

reactor safety
112
17
43
172
0.59

reliability
47
5
10
62
0.36

waste
87
5
26
118
0.80

weapons
52
11
15
78
0.77

other
120
23
29
172
0.49

all
858
123
245
1226
0.62

pro

706

cons

520

Additionally, it contains 2000 unlabeled sentences with presumably argumentative content sampled from the newspaper “The Guardian”.

[1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1402