Search Results for author:
Found 15 papers, 10 papers with code
De-Bias for Generative Extraction in Unified NER Task
Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence.
Information Re-Organization Improves Reasoning in Large Language Models
In this paper, we propose an information re-organization (InfoRE) method before proceeding with the reasoning to enhance the reasoning ability of LLMs.
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Large Language Models exhibit robust problem-solving capabilities for diverse tasks.
An Expression Tree Decoding Strategy for Mathematical Equation Generation
To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others.
Ranked #2 on
Math Word Problem Solving
on MathQA
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.
Ranked #1 on
Nested Named Entity Recognition
on ACE 2004
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
Query-based Instance Discrimination Network for Relational Triple Extraction
Joint entity and relation extraction has been a core task in the field of information extraction.
Contrastive Learning
Joint Entity and Relation Extraction
+1
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem
Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation.
Ranked #1 on
Math Word Problem Solving
on Math23K
(using extra training data)
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations.
Parallel Instance Query Network for Named Entity Recognition
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+5
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model
In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children.
Ranked #3 on
Constituency Parsing
on CTB5
A Sequence-to-Set Network for Nested Named Entity Recognition
We utilize a non-autoregressive decoder to predict the final set of entities in one pass, in which we are able to capture dependencies between entities.
Ranked #6 on
Nested Named Entity Recognition
on ACE 2005
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition.
Ranked #6 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+3
