Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of churn reduction with only a modest increase in resource usage.
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands.
Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of jitter reduction for semantic parsing systems with only a modest increase in resource usage.
Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983).
Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory.
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence.
Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them.
An idiom is defined as a non-compositional multiword expression, one whose meaning cannot be deduced from the definitions of the component words.
We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information.
Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e. g., claims, premises) and their argumentative relations (e. g., support, attack).
This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets.