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Found 10 papers, 7 papers with code
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
A major challenge facing the world is the provision of equitable and universal access to quality education.
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion
Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme.
Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections
While there has been substantial progress in text comprehension through simple factoid question answering, more holistic comprehension of a discourse still presents a major challenge (Dunietz et al., 2020).
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.
Generating Dialogue Responses from a Semantic Latent Space
Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary.
Inquisitive Question Generation for High Level Text Comprehension
Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems.
Assessing Discourse Relations in Language Generation from GPT-2
Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models.
Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation
Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses.
Domain Agnostic Real-Valued Specificity Prediction
Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse.
Learning Deep Latent Spaces for Multi-Label Classification
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance.
