Search Results for author:
Found 11 papers, 7 papers with code
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs
With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge.
EmotionFlow: Capture the Dialogue Level Emotion Transitions
However, the spread impact of emotions in a conversation is rarely addressed in existing researches.
Ranked #14 on
Emotion Recognition in Conversation
on MELD
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary.
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE.
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.
AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods.
Data Augmentation for Copy-Mechanism in Dialogue State Tracking
While several state-of-the-art approaches to dialogue state tracking (DST) have shown promising performances on several benchmarks, there is still a significant performance gap between seen slot values (i. e., values that occur in both training set and test set) and unseen ones (values that occur in training set but not in test set).
TransSent: Towards Generation of Structured Sentences with Discourse Marker
Automatic and human evaluation results show that TransSent can generate structured sentences with high quality, and has certain scalability in different tasks.
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer
So we propose a two step approach "Mask and Infill".
ESA: Entity Summarization with Attention
Entity summarization aims at creating brief but informative descriptions of entities from knowledge graphs.
Conditional BERT Contextual Augmentation
BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model.
