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Found 11 papers, 9 papers with code
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
The metaphor identification module is able to perform a self-training procedure, which discovers novel metaphors from a large-scale unlabeled corpus for NM generation.
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation
Data contamination in model evaluation is getting increasingly prevalent as the massive training corpora of large language models often unintentionally include benchmark samples.
Metaphor Detection via Explicit Basic Meanings Modelling
One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design.
Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering
Large language models (LLMs) have received significant attention by achieving remarkable performance across various tasks.
Metaphor Detection with Effective Context Denoising
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection.
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection.
The Secret of Metaphor on Expressing Stronger Emotion
Metaphors are proven to have stronger emotional impact than literal expressions.
Nominal Metaphor Generation with Multitask Learning
Metaphor generation is a challenging task which can impact many downstream tasks such as improving user satisfaction with dialogue systems and story generation.
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss.
Hierarchical Region Learning for Nested Named Entity Recognition
Named Entity Recognition (NER) is deeply explored and widely used in various tasks.
GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring
We also applied GPU-FV for realtime video monitoring tasks and found that GPU-FV outperforms a number of previous works.
