This repository contains TT-SEMPRE, a fork of SEMPRE. TT-SEMPRE is a semantic parser that understands virtual assistant commands of the form when-get-do
Download dependencies:
./pull-dependencies core corenlp thingtalk
Build the core:
JAVAHOME=<path to java> ant thingtalk
Build the HTTP server:
JAVAHOME=<path to java> ant api
JAVAHOME should be set to the path to your Java installation, eg. /usr/lib/jvm/openjdk-1.8.0.
Java 1.8 is required. A working C compiler must also be installed.
./run.sh interactive -ThingpediaDatabase.dbPw <DATABASE_PW> -ThingpediaLexicon.subset <SUBSET>
Where <DATABASE_PW> is the password to the Thingpedia Database, and <SUBSET> is a space separated
list of devices to limit the scope of Thingpedia (e.g. twitter instagram).
In interactive mode, it is possible to type sentences and check how they are parsed.
./run-training.sh -ThingpediaDatabase.dbPw <DATABASE_PW> -ThingpediaLexicon.subset <SUBSET>
This command will fetch the data from the Thingpedia dataset and run a full session of training.
The trained model (.params file) will be saved under ./almond.
You must have the Berkeley Aligner installed. By default the script looks in the parent directory
of the TT-SEMPRE checkout. Set the environment variable BERKELEYALIGNER if you installed it
elsewehre.
You can specify the environment variables TRAINING and TESTING (as a comma separated list of
Thingpedia dataset names, eg thingpedia,online,turking-prim0) to control the datasets used
for training and testing. The default covers the normal Thingpedia dataset used to train Almond.
If you specify the environment variables SEMPRE_USER and SEMPRE_SERVER, the trained model
will be uploaded to the given server (using the given user through ssh) and the server will
be reloaded.
./run.sh server -ThingpediaDatabase.dbPw <DATABASE_PW> -ThingpediaLexicon.subset <SUBSET>
The server runs on port 8400 by default. Use -APIServer.port <X> and -APIServer.ssl_port <X>
to change the ports and to enable TLS.
http://127.0.0.1:8400/query?q=<query>&locale=<lang>&limit=<x>
Set <query> to the sentence to parse, <lang> to the locale code (eg. en-US) and <x> to the
maximum number of results to report. If limit is unspecified it defaults to 20. If the locale
is unspecified it defaults to en-US (American English).
Result:
{"sessionId":"....",
"candidates":[
{"prob":0.5,"score":1,"answer":"..."},
{"prob":0.5,"score":1,"answer":"..."},
]}