Search Results for author:
Found 20 papers, 14 papers with code
Few-NERD: A Few-Shot Named Entity Recognition Dataset
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Ranked #5 on
Named Entity Recognition (NER)
on Few-NERD (SUP)
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval
We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain.
Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval
Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task.
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
Probing BERT in Hyperbolic Spaces
We introduce a Poincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies.
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually.
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
AISHELL-NER: Named Entity Recognition from Chinese Speech
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size.
Ranked #1 on
Passage Re-Ranking
on MS MARCO
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging
Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction
In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types.
Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task
This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No.
Ranked #1 on
2D Human Pose Estimation
on Alibaba Cluster Trace
(using extra training data)
Hierarchy-Aware Global Model for Hierarchical Text Classification
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
Prompt-Learning for Fine-Grained Entity Typing
In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios.
Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track
Large-scale text retrieval technology has been widely used in various practical business scenarios.
