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Found 8 papers, 3 papers with code
Exploring Continual Learning of Compositional Generalization in NLI
In this paper, we introduce the Continual Compositional Generalization in Inference (C2Gen NLI) challenge, where a model continuously acquires knowledge of constituting primitive inference tasks as a basis for compositional inferences.
Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation
Hence, we propose a dynamic modularized reasoning model, MORSE, to improve the compositional generalization of neural models.
Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks
Coherence is an important aspect of text quality, and various approaches have been applied to coherence modeling.
SETI: Systematicity Evaluation of Textual Inference
We propose SETI (Systematicity Evaluation of Textual Inference), a novel and comprehensive benchmark designed for evaluating pre-trained language models (PLMs) for their systematicity capabilities in the domain of textual inference.
RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy
In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data.
MM-AVS: A Full-Scale Dataset for Multi-modal Summarization
Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks.
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labelling tasks due to their respective strengths.
Multi-modal Summarization for Video-containing Documents
Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth.
