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Found 41 papers, 27 papers with code
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.
Exploring Lottery Prompts for Pre-trained Language Models
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning.
Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense Passage Retrieval
Recently, various studies have been directed towards exploring dense passage retrieval techniques employing pre-trained language models, among which the masked auto-encoder (MAE) pre-training architecture has emerged as the most promising.
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
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
+3
Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
COMBO: A Complete Benchmark for Open KG Canonicalization
The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized.
MGeo: Multi-Modal Geographic Pre-Training Method
Single-modal PTMs can barely make use of the important GC and therefore have limited performance.
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing.
Ranked #1 on
Entity Typing
on Open Entity
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field
We use mean-field variational inference for efficient type inference on very large type sets and unfold it as a neural network module to enable end-to-end training.
Ranked #2 on
Entity Typing
on Open Entity
Named Entity and Relation Extraction with Multi-Modal Retrieval
MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.
Ranked #1 on
Multi-modal Named Entity Recognition
on SNAP (MNER)
Multi-modal Named Entity Recognition
Named Entity Recognition
+3
Few-shot Classification with Hypersphere Modeling of Prototypes
Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere.
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.
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
We apply BABERT for feature induction of Chinese sequence labeling tasks.
Ranked #1 on
Chinese Word Segmentation
on MSRA
Chinese Named Entity Recognition
Chinese Word Segmentation
+3
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios.
DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking
Speech Entity Linking aims to recognize and disambiguate named entities in spoken languages.
Domain-Specific NER via Retrieving Correlated Samples
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
Adversarial Self-Attention for Language Understanding
Deep neural models (e. g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness.
Ranked #1 on
Machine Reading Comprehension
on DREAM
Machine Reading Comprehension
Named Entity Recognition (NER)
+4
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
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.
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
+4
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.
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition
Named Entity Recognition
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
Few-shot Learning with Big Prototypes
A big prototype could be effectively modeled by two sets of learnable parameters, one is the center of the hypersphere, which is an embedding with the same dimension of training examples.
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.
Counterfactual Inference for Text Classification Debiasing
In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.
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.
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
Few-shot NER
on Few-NERD (INTRA)
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.
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.
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.
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.
A Neural Multi-digraph Model for Chinese NER with Gazetteers
Gazetteers were shown to be useful resources for named entity recognition (NER).
Better Modeling of Incomplete Annotations for Named Entity Recognition
Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information.
DM\_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution
This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019.
Neural Chinese Address Parsing
This paper introduces a new task {--} Chinese address parsing {--} the task of mapping Chinese addresses into semantically meaningful chunks.
DM\_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features
This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP.
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)
