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Found 14 papers, 6 papers with code
Reducing Model Churn: Stable Re-training of Conversational Agents
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.
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands.
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments
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.
ENTRUST: Argument Reframing with Language Models and Entailment
Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983).
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
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.
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
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.
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them.
Confirming the Non-compositionality of Idioms for Sentiment Analysis
An idiom is defined as a non-compositional multiword expression, one whose meaning cannot be deduced from the definitions of the component words.
Fixed That for You: Generating Contrastive Claims with Semantic Edits
Understanding contrastive opinions is a key component of argument generation.
Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks
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.
Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum
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).
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification
This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets.
