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Found 7 papers, 1 papers with code
Do Massively Pretrained Language Models Make Better Storytellers?
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase
We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks.
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
In this work, we propose a novel approach that: (i) learns to generate additional slot type specific features in order to improve accuracy and (ii) provides explanations for slot filling decisions for the first time in a joint NLU model.
Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting
Most existing stylistic text rewriting methods and evaluation metrics operate on a sentence level, but ignoring the broader context of the text can lead to preferring generic, ambiguous, and incoherent rewrites.
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
To study the contextual dynamics of offensiveness, we train models to generate COBRA explanations, with and without access to the context.
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Counterspeech, i. e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship.
