This is the repository of a pre-training event factuality detection library which implements a deep learning method to quantify the factuality of an event (i.e., did it happen or not). To account for different components (including lexical, syntactic, and structural information) expressing the factuality nature of influence statements, we re-implement recent neural models of factuality (Rudinger et al., 2018) in Scala and package the software as a standalone library.

This repository contains:

• A factuality library in the top-level project which provides in org.clulab.factuality.Factuality the neural model (mostly a standard 2-layer bidirectional linear chain LSTM followed by a 2-layer regression model) and training and prediction procedures needed for both factuality developers and clients.

• A factuality-developer subproject for training the model (reading conll training and dev files and fitting to them) in FactualityTrainer and for evaluating the model against test files in FactualityPredictor.

• A factuality-client example which demonstrates how to load the pre-trained model and use it to calculate a factuality score for a tokenized influence statement. The code and build files can be copied to other projects.

• A factuality-models directory for use in publishing and releasing factuality models.

To use the published factuality library and the pretrained model, you do not need to "install" or clone this repo. The factuality library has been published and can be incorporated into your project by adding the following dependencies to your build.sbt file:

resolvers += "Artifactory" at "http://artifactory.cs.arizona.edu:8081/artifactory/sbt-release"

libraryDependencies ++= Seq(
  "org.clulab" %% "factuality" % "1.0.0",
  "org.clulab" % "factuality-models" % "0.2.0"
)

The factuality library is available through regular channels that dependency managers like sbt and maven consult, but the additional resolver is needed for the large models can't be distributed through those same channels.

If you simply want to access the factuality library in your project, add the above library dependencies to your dependency manager and use the API demonstrated below to evaluate the factuality of an event in a given sentence:

import org.clulab.factuality.Factuality
  ...
  // This particular model is provided in the library dependency.
  val factuality = Factuality("org/clulab/factuality/models/FTrainFDevScim3")
  val words = "Parthenolide induced apoptosis and inhibited cell proliferation and the expression of VEGF in vitro .".split(' ')
  val predicateIndex = 1 // induced
  val prediction: Float = factuality.predict(words, predicateIndex)

  println(s"Prediction: $prediction")
}               

The factuality model is quite large, so it's important to configure Java with access to enough memory. -Xmx12G is the suggested value. See also the file .sbtopts for other Java options. It will load slowly, so batching predictions is advised for efficiency. In this example, the model is contained in a resource called FTrainFDevScim3. The names of the models are subject to change and developers may want to rename their own resources to distinguish models.

If you did go ahead and clone the repo, the factuality-client example program can be run with

sbt:factuality> factuality-client/run

Developers should probably clone this repo:

$ git clone http://github.com/clulab/factuality
$ cd factuality

This is a standard sbt project, so use the usual commands (i.e., compile, run, assembly, etc.) to manipulate it (or open it with IntelliJ). Training will likely require an HPC, so some of the examples below are geared toward that.

$ sbt
sbt:factuality> factuality-developer/compile

If sbt is installed on your computer/HPC you can train the model with

sbt:factuality> factuality-developer/runMain org.clulab.factuality.FactualityTrainer -train train.conll -dev dev.conll -test test.conll -model model -embed org/clulab/glove/glove.42B.300d.txt

If you cannot use sbt interactively, quote all the arguments and send them en masse to sbt.

$ sbt 'factuality-developer/runMain org.clulab.factuality.FactualityTrainer -train train.conll -dev dev.conll -test test.conll -model model -embed org/clulab/glove/glove.42B.300d.txt'

If you have no access to sbt on the computer that does the training, but there is at least Java (e.g., on some HPCs), the project needs to be assembled on a computer that does have sbt, and then the assembly can be moved to the training computer. Run

sbt:factuality> factuality-developer/assembly

locally, and then copy the file factuality-developer-assembly-1.0.0-SNAPSHOT.jar (or similar, depending what version you're on) in directory factuality-developer/target/scala-2.12/ to the other computer. There run

$ java -cp factuality-developer-assembly-1.0.0-SNAPSHOT.jar org.clulab.factuality.FactualityTrainer -train train.conll -dev dev.conll -test test.cnll -model model -embed org/clulab/glove/glove.42B.300d.txt

In these examples, the train, dev, and test "files" are really resources that are included in the project. glove.42B.300d.txt is also a resource and is taken from the glove dependency. The model will be output in files model.rnn and model.x2i.

If you want to use different data sets, it is possible to override the conll resources and specify a local file instead. The following command reads the files from the disk that happen to be supplied with the source code. You could use other ones.

sbt:factuality> factuality-developer/runMain org.clulab.factuality.FactualityTrainer -train factuality-developer/src/main/resources/train.conll -dev factuality-developer/src/main/resources/dev.conll -test factuality-developer/src/main/resources/test.cnll -model model -embed org/clulab/glove/glove.42B.300d.txt

To evaluate your model after training, use the FactualityPredictor program. If you have just generated a model called model, then the command would be

sbt:factuality> factuality-developer/runMain org.clulab.factuality.FactualityPredictor -test test.conll -model model

The factuality-client can also use the new model via val factuality = Factuality("model").

For tips on deploying, publishing, and releasing the models, see the factuality-models subproject.