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ReCAP: Argument Graph Retrieval

DOI

This program has been used to perform the evaluation for my Bachelor's Thesis. It provides a retrieval for argumentation graphs.

System Requirements

  • Docker and Docker-Compose

Installation

Application

Duplicate the file config_example.yml to config.yml and adapt the settings to your liking. Please do not edit the webserver settings as Docker depends on them.

Embeddings

The following list contains all models used in the paper together with instructions to make them usable for the software. It is recommended to rename the files to a memorable name and put them in a folder named data/embeddings.

  • Google Word2Vec:
    • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality (2013), https://arxiv.org/abs/1310.4546
    • docker-compose run --rm app python -m recap_agr.cli.convert bytes-text path/to/GoogleNews-vectors-negative300.bin.gz
    • docker-compose run --rm app python -m recap_agr.cli.convert model-gensim path/to/GoogleNews-vectors-negative300.txt
  • Custom Doc2Vec: Not yet available.
  • fastText:
    • Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching Word Vectors with Subword Information (2016), https://arxiv.org/abs/1607.04606
    • Unpack the file.
    • docker-compose run --rm app python -m recap_agr.cli.convert model-gensim path/to/crawl-300d-2M.vec
  • GloVe:
    • Pennington, J., Socher, R., Manning, C.: Glove: Global Vectors for Word Representation. In: Proceedings of EMNLP (2014). https://doi.org/10.3115/v1/D14-1162
    • Unpack the file.
    • Run cat path/to/glove.6B.300d.txt | wc -l to obtain the number of items.
    • Add #LINES 300 as the first line of the file, e.g. 1000 300 if the output above gave 1000 (recommended to use vim).
    • docker-compose run --rm app python -m recap_agr.cli.convert model-gensim path/to/glove.6B.300d.txt
  • Infersent:
    • Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (2017), https://arxiv.org/abs/1705.02364
    • No modification needed.
  • USE-D:
    • Cer, D., Yang, Y., Kong, S.y., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., Sung, Y.H., Strope, B., Kurzweil, R.: Universal Sentence Encoder (2018), http://arxiv.org/abs/1803.11175
    • Unpack the file.
  • USE-T:
    • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems 30, pp. 5998–6008 (2017)
    • Unpack the file.

Usage

It is possible to run the software with

docker-compose up

This will download all required data on the first run and thus may take a while. Future runs are cached and the app available immediately.

The webserver is then accessible on http://localhost:8888

Data Folder Contents

The following folders need to be specified:

  • casebase_folder
  • queries_folder
  • embeddings_folder
  • candidates_folder
  • results_folder

Case-Base and Queries

All files need to be present in the AIF- or OVA-format (and thus be .json files).

Embeddings

Only the native gensim format is supported.

Results

No file needs to be put in here. The exporter will write the results to this folder. However, the folder needs to be created manually.

Candidates

For each query, a candidates file with the following content has to be provided so that the evaluation metrics are calculated.

Please note: Candidates and rankings do not need to contain the same filenames.

{
	"candidates": [
		"nodeset6366.json",
		"nodeset6383.json",
		"nodeset6387.json",
		"nodeset6391.json",
		"nodeset6450.json",
		"nodeset6453.json",
		"nodeset6464.json",
		"nodeset6469.json"
	],
	"rankings": {
		"nodeset6366.json": 2,
		"nodeset6383.json": 2,
		"nodeset6387.json": 3,
		"nodeset6391.json": 2,
		"nodeset6450.json": 2,
		"nodeset6453.json": 2,
		"nodeset6464.json": 2,
		"nodeset6469.json": 1
	}
}

Important Information

About

Code for the paper published at ICCBR 2019 (Otzenhausen)

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