We provide three scripts for evaluation.
This calculates precision, recall, and F-score over edges.
python3 tools/evaluation/graph-eval.py --gold gold-files --auto system-files
The expected format for output files is:
anything.../filename:line-number line-number -
blah-blah/2004-11-15.annotation.txt:1000 1000 - blah-blah/2004-11-15.annotation.txt:1001 1001 - blah-blah/2004-11-15.annotation.txt:1002 1002 - blah-blah/2004-11-15.annotation.txt:1003 1002 - yaddah_yaddah/2004-11-15.annotation.txt:1004 1003 - yaddah_yaddah/2004-11-15.annotation.txt:1005 1003 -
This calculates a range of cluster metrics. It requires the Google OR Tools.
python3 tools/evaluation/conversation-eval.py gold-file system-file
The expected format for output files has each cluster on one line:
anything.../filename:line-number line-number line-number line-number ...
blah-blah/2004-11-15.annotation.txt:1000 blah-blah/2004-11-15.annotation.txt:1001 yaddah_yaddah/2004-11-15.annotation.txt:1002 1003 1004 1005
It also has the option to specify the set of metrics to use.
DSTC 8 Evaluation
This is the code being used in DSTC 8. It is run with:
python3 tools/evaluation/dstc8-evaluation.py --gold data/dev/*anno* --auto example-run.out
For a model trained as described in the source code README, that should give:
92.15 1 - Scaled VI 74.19 Adjusted rand index 40.53 Matched clusters precision 41.26 Matched clusters recall 40.89 Matched clusters f-score
The expected format is the same as for
This is the code we used to calculate agreement between annotators. It expects to receive a list of filenames as arguments, where files are named:
Results are printed for each pair of annotators.
This uses a permutation test, comparing two sets of annotations. It is run with:
./tools/evaluation/significance.py \ gold-file --auto0 system0-output-files --auto system1-output-files
These are a set of tools for converting annotations:
|output-from-py-to-graph.py||Convert the output of the model into our graph format.|
|graph-to-cluster.py||Convert a set of links into a set of conversations with Union-Find.|
|cluster-to-messages.py||Print out the actual messages that a set of conversations correspond to.|
|graph-to-messages.py||Do both graph-to-cluster and cluster-to-message at once.|
|output-from-cpp-to-graph.py||Convert the output of the C++ model into our graph format.|
Download IRC data
./download-raw-data.sh will download all available IRC data from the Ubuntu channel.
Note, this includes some days in which there is no data or the files have other issues.
Convert to ASCII
We converted downloaded files to ascii in two steps:
make-txt.pyto convert as much as possible (this will handle most cases of things like accents).
tr -cd '[:print:]\\n\\t' < filename > filename.asciito delete remaining non-ascii characters.
We have two tokenisation scripts (preliminary experiments with off-the-shelf tokenisers gave poor results). The easiest one to run is the one we’ve prepared for DSTC 8:
./dstc8-tokenise.py --vocab vocab.txt --output-suffix .tok ASCII-FILENAME
The other (
tokenise.py) has additional options that are generaly not needed.
Run to convert text to tokens with unk symbols.
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