Multi-task neural network for toxicity detection

A computing system to train a multi-task neural network to perform toxicity detection of user-generated textual content, the computing system being configured to:

• obtain one or more training examples, each training example comprising input data comprising a representation of user-generated textual content and a target output for each of one or more tasks out of a plurality of tasks, wherein each of the plurality of tasks is to identify a respective attribute in user-generated textual content, one of the attributes being toxicity;
• perform a training step to train the multi-task neural network on a current task of the plurality of tasks, comprising for each of the training examples:
• generating an initial encoding for the user-generated textual content, comprising processing the input data using an initial encoder of the multi-task neural network;
• generating an attribute feature representation for each of the plurality of attributes, comprising processing the initial encoding using a respective attribute feature extractor of the multi-task neural network associated with the attribute, wherein one of the attribute feature representations is a current task attribute feature representation for the current task that is generated by a current task attribute feature extractor;
• generating a common feature representation, comprising processing the initial encoding using a task-common feature extractor of the multi-task neural network;
• generating a task discriminator output representing a prediction of which task the multi-task neural network is currently being trained to perform in the training step, comprising processing the common feature representation using a gradient reversal layer and a task discriminator;
• generating a combined feature representation, comprising combining the current task attribute feature representation with the common feature representation;
• generating, by an output portion of the multi-task neural network associated with the current task, a plurality of outputs for the current task, the plurality of outputs comprising:
• a specific output generated by processing the current task attribute feature representation;
• a combined output generated by processing the combined feature representation;
• and
• one or more adversarial outputs, each generated by processing an attribute feature representation that is not the current task attribute feature representation using a gradient reversal layer;
• and
• update parameters of the multi-task neural network to:
• (i) minimize a measure of difference between the specific output and the target output for the current task (ii) minimize a measure of difference between the combined output and the target output for the current task, (iii) maximize a measure of difference between each of the one or more adversarial outputs and the target output for the current task, and (iv) maximize a measure of difference between the task discriminator output and a current task identifier indicating the current task.

A non-transitory computer-readable medium storing instructions, which when executed by a processor, cause the processor to:

• obtain input data comprising a representation of user-generated textual content;
• and
• generate a toxicity prediction and a prediction for each of one or more attributes for the user-generated textual content by processing the input data using a multi-task neural network, the processing comprising:
• generating an initial encoding for the user-generated textual content, comprising processing the input data using an initial encoder of the multi-task neural network;
• generating a toxicity feature representation, comprising processing the initial encoding using a task-specific toxicity feature extractor of the multi-task neural network;
• generating an attribute feature representation for each of the one or more attributes, comprising, for each attribute, processing the initial encoding using a respective task-specific attribute feature extractor of the multi-task neural network associated with the attribute;
• generating a common feature representation, comprising processing the initial encoding using a task-common feature extractor of the multi-task neural network;
• generating a plurality of combined feature representations, comprising combining each of the toxicity feature representation and the one or more attribute feature representations with the common feature representation;
• generating the toxicity prediction by processing, using a toxicity output portion of the multi-task neural network, the combined feature representation formed from combining the toxicity feature representation with the common feature representation;
• and
• generating the prediction for each of the one or more attributes by processing, using a respective output portion of the multi-task neural network associated with the attribute, the combined feature representation formed from combining the attribute feature representation for the attribute with the common feature representation.