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Found 9 papers, 5 papers with code
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations
To address such a problem, this paper proposes a novel efficient entities and relations extraction model called TDEER, which stands for Translating Decoding Schema for Joint Extraction of Entities and Relations.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
2D Matryoshka Sentence Embeddings
The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.
Generative Deduplication For Socia Media Data Selection
By doing so, it can improve social media language understanding performance and save training time.
BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings
Most recent studies employed large language models (LLMs) to learn sentence embeddings.
Label Supervised LLaMA Finetuning
We evaluate this approach with Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU.
Ranked #1 on
Named Entity Recognition (NER)
on CoNLL03
(F1 (micro) metric)
AnglE-optimized Text Embeddings
This novel approach effectively mitigates the adverse effects of the saturation zone in the cosine function, which can impede gradient and hinder optimization processes.
Ranked #1 on
Semantic Textual Similarity
on STS16
Recurrent Attention Networks for Long-text Modeling
Revisiting the self-attention mechanism and the recurrent structure, this paper proposes a novel long-document encoding model, Recurrent Attention Network (RAN), to enable the recurrent operation of self-attention.
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP
The minimization problem is accordingly turned into a QMI problem, and the problem is solved by a restricted linear matrix inequality relaxation with additional valid convex constraints.
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification
The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e. g., hand-craft features and domain expert knowledge).
