Search Results for author:
Found 15 papers, 9 papers with code
SHARP: Search-Based Adversarial Attack for Structured Prediction
Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations.
Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space
We introduce LatentSeek, a novel framework that enhances LLM reasoning through Test-Time Instance-level Adaptation (TTIA) within the model's latent space.
From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation up to 100K Tokens
While traditional speculative decoding methods exist, simply extending their generation limits fails to accelerate the process and can be detrimental.
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers
Accommodating long sequences efficiently in autoregressive Transformers, especially within an extended context window, poses significant challenges due to the quadratic computational complexity and substantial KV memory requirements inherent in self-attention mechanisms.
LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments
Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs.
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches.
Document-level Relation Extraction
Multi-Label Classification
+3
RAM: Towards an Ever-Improving Memory System by Learning from Communications
We introduce an innovative RAG-based framework with an ever-improving memory.
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document representations.
DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
Also, we discover that the limited context length causes the retrieval knowledge to be invisible to the model.
Multilingual Named Entity Recognition
named-entity-recognition
+4
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field
Prior works on joint Information Extraction (IE) typically model instance (e. g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding.
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-17
Multi-modal Named Entity Recognition
named-entity-recognition
+1
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task.
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}.
