This repository represents the attempt for the Touché Task 3: Image Retrival for Arguments by the group Aramis.

You need Docker to setup this project. If you are not familiar with Docker, please visit the linked tutorial.

Clone this repository and create a docker image with the Dockerfile. This image contains the entrypoint to the startup.py. The program needs three directories to work correctly:

• an input directory where the data is located (default: ./data)
• a working directory where the index and other stuff is saved for multiple use (default: ./working)
• an output directory where the results are saved (default: ./out)

It's possible to set the directories in the config.json, if so the config.json - PATH has to be the parameter after -cfg.

The data can be structured in two different ways. If you download the data from the Touché website it is splited in six different directories with different parts of each image id. You can just unzip those files and move them to the input directory. But you can also merge these six directories into one where only the image subdirectory is left. Then you have to pass the -f parameter to the program.

If you use Docker, all the requirements (including tesseract) are included in the docker-image and the following part is not important to get the system started. But we would like to mention the used technologies and the possibility to use image-detection as stand-alone, without the retrieval-system.

The image-detection is only needed during indexing. To run the image-detection without docker you have can download the tesseract5-installer directly or from the Uni-Mannheim-page and install it. Please use tesseract 5 or higher. In config the setting on_windows must be True.

You can install the application directly into \properties or install it anywhere and copy the tesseract folder into \properties. Check, if the \properties\tesseract\tesseract.exe exists, but if you have done everything correctly this file should be there.

The programm has different functions:

• Indexing -idx
• Retrieval run -qrel + -mtag {method tag}
• Web application with search/evaluation interface -web

In the script scripts/tira-run.sh is an example docker run, where only the input/output directories and the function parameter are needed. To start for example the indexing process run sh ./tira-run.sh -i $inputDir -o $outDir -idx. For a complete view of the possible parameter use -help. We provide our final models in working/models/.

The method tag aramis#{ArgumentModel}#{StanceModel}#w{topic_weight} for retrieval run has three parameters:

• ArgumentModel: standard or NN_{model_name} where model_name is the name of a trained neural net
• StanceModel: standard or NN_{model_name} where model_name is the name of a trained neural net
• Topic weight: a float in [0,1] wich represents the use of the topic score in the retrieval process

Our program offers an evaluation website (located under 0.0.0.0/evaluation). If you don't see images, please check if you set a username in the top right corner.

We evaluated ~9500 images from the Touché22 Task3 data. These evaluation can be found in working/image_eval.txt. The users where anonymized. A description of the labels can be found in out paper.

In the analysis_labeled_data.md file is the result of our analysis of the Touché dataset. For details check the paper.