This is the data and code for the paper:

"SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference"
Martin Schmitt and Hinrich Schütze. ACL 2019. paper

Additional material (e.g., slides of the talk) can be found here.

@inproceedings{schmitt2019sherliic,
    title = "{S}her{LI}i{C}: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference",
    author = {Schmitt, Martin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1086",
    pages = "902--914"
}

The SherLIiC resources can be downloaded from here.

You should extract the archive to the folder data.

The embedding files in embeddings/filtered only contain embeddings for the relations in SherLIiC-dev and SherLIiC-test.

Full embedding files (i.e. embeddings for all entities and relations in the whole SherLIiC event graph) can be downloaded here.

To see a list of all available baselines, run:

python3 code/baselines.py list_baselines

python3 code/baselines.py single  --help
Usage: baselines.py single [OPTIONS] BASELINE [DATASET]...

  Runs BASELINE on (a list of) DATASETs (See `list_baselines` for a list of
  available baselines).

Options:
  --examples / --no-examples  Whether or not to print example predictions
                              (default: false).
  --use-lemma / --no-lemma    Whether to make predictions on top of `Lemma`
                              baseline or not. (default: use it)
  --rounding / --no-rounding  Whether or not results should be rounded
                              (default: true).
  -t, --threshold FLOAT       Threshold for tunable baselines (default: 0.5);
                              ignored for non-tunable baselines.
  --help                      Show this message and exit.

To evaluate all non-tunable baselines on dev and test, run:

python3 code/baselines.py non_tunables data/dev.csv data/test.csv

The results will be stored to non-tunable-dev.txt and non-tunable-test.txt.

For more options, see python3 code/baselines.py non_tunables --help.

To tune all tunable baselines on dev and then evaluate on dev and test, run:

python3 code/baselines.py tunables data/dev.csv data/test.csv 

The results will be stored to tunable-devtest.txt.

For more options see python3 code/baselines.py tunables --help.

To find the F1-optimal threshold for a single baseline on a given dataset (which should be dev, of course), run:

python3 code/baselines.py find_threshold DATASET BASELINE

Example:

python3 code/baselines.py find_threshold data/dev.csv typed_rel_emb

For the baseline w2v+tsg_rel_emb, the effectiveness of type-informed vs. unrestricted (untyped) relation embeddings has to be determined before-hand. For this run:

python3 code/baselines.py tsg_pref data/dev.csv tsg_typed_vs_untyped.txt

This will store the type signature preferences in the file tsg_typed_vs_untyped.txt. To use this file, you have to enter the path to it in file_paths.json.

A precomputed file is available in data/tsg_typed_vs_untyped_thr0.0-only-dev.txt. So w2v+tsg_rel_emb can be used right away without any preprocessing necessary (i.e., other than downloading the pretrained word2vec embeddings).

If you want to qualitatively analyze errors made by several tunable baselines on a specific dataset (which should be dev), you can run

python3 code/baselines.py error_analysis DATASET OUT_FILE [METHODS]...

where results will be written to OUT_FILE. You can specify as many METHODS as you want.

The method to convert the SherLIiC Event Graph to a training corpus suitable for word2vec is given by the command create_training:

python3 code/baselines.py create_training data/teg.tsv typed_rel_emb_train.txt

The command above would create the training corpus used for learning the embeddings in embeddings/complete/typed_rel_emb.txt, i.e., the embeddings for the baseline typed_rel_emb.

See python3 code/baselines.py create_training --help for more options.

In order to use the word2vec baseline (and all baselines building on it), you have to

1. Download the pretrained word embeddings from here.
2. Enter the path to the gzipped file in file_paths.json under the key word2vec.

In order to reproduce the rule collection baselines, you have to download them, sometimes run a preprocessing script and enter the path to the right file into file_paths.json. Find specific instructions below.

1. Download ACL2011Resource.zip from here.
2. Unzip it and enter the path to ResourceEdges.txt in file_paths.json under the key berant.

1. Download reverb_local_global.rar from here.
2. Extract the archive and enter the path to reverb_local_clsf_all.txt in file_paths.json under the key berant_new.

1. Download PPDB 2.0 XXXL All from here.
2. Run python3 code/preprocess.py ppdb path/to/ppdb-2.0-xxxl-all.gz external/ppdb.csv
3. Enter the path external/ppdb.csv in file_paths.json under the key ppdb.

1. Download patty-dataset-freebase.tar.gz from here.
2. Extract the archive and enter the paths to wikipedia-patterns.txt and wikipedia-subsumptions.txt as two-element list in file_paths.json under the key patty.

1. Download sherlockrules.zip from here.
2. Extract the archive, enter the folder sherlockrules and run

cat sherlockrules.* | grep -v '^#' | cut -f1,2,9 | grep -v '2\.0' | cut -f1,3 > sherlockrules.all

3. Enter the path to sherlockrules.all in file_paths.json under the key schoenmackers.

1. Download resource.zip from here.
2. Unzip it and enter the path to rules.tsv in file_paths.json under the key chirps.

Once you have downloaded all resources and put the right paths into file_paths.json, you should be able to run this baseline, too.