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@@ -67,11 +67,22 @@ German Legal Sentences (GLS) is an automatically generated training dataset for
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  ### Supported Tasks and Leaderboards
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
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  ### Languages
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- The text in this dataset is in english
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  ## Dataset Structure
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  #### Who are the source language producers?
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- [More Information Needed]
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  ### Annotations
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  ### Personal and Sensitive Information
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- [More Information Needed]
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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- [More Information Needed]
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  ### Discussion of Biases
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  ### Citation Information
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- [More Information Needed]
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  ### Contributions
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  ### Supported Tasks and Leaderboards
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+ The main associated task is *Semantic Similarity Ranking*. We propose to use the *Mean Reciprocal Rank* (MRR) cut at the tenth position as well as MAP and Recall on Rankings of size 200. As baselines we provide the follows:
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+ | Method | MRR@10 | MAP@200 | Recall@200 |
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+ |-----------------------------------|---------:|-----------:|------------:|
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+ | BM25 - default `(k1=1.2; b=0.75)` | 25.7 | 17.6 | 42.9 |
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+ | BM25 - tuned `(k1=0.47; b=0.97)` | 26.2 | 18.1 | 43.3 |
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+ | [CoRT](https://arxiv.org/abs/2010.10252) | 31.2 | 21.4 | 56.2 |
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+ | [CoRT + BM25](https://arxiv.org/abs/2010.10252) | 32.1 | 22.1 | 67.1 |
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+ In addition, we want to support a *Citation Recommendation* task in the future.
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+ If you wish to contribute evaluation measures or give any suggestion or critique, please write an [e-mail](mailto:marco.wrzalik@hs-rm.de).
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  ### Languages
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+ This dataset contains texts from the specific domain of German court decisions.
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  ## Dataset Structure
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  #### Who are the source language producers?
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+ The source language originates in the context of German court proceedings.
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  ### Annotations
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  ### Personal and Sensitive Information
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+ The source documents are already public and anonymized.
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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+ With this dataset, we strive towards better accessibility of court decisions to the general public by accelerating research on semantic search technologies. We hope that emerging search technologies will enable the layperson to find relevant information without knowing the specific terms used by lawyers.
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  ### Discussion of Biases
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  ### Citation Information
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+ Coming soon!
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  ### Contributions
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