Search Results for author:
Found 29 papers, 21 papers with code
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
Chinese Spelling Check (CSC) is to detect and correct Chinese spelling errors.
Rethinking Label Smoothing on Multi-hop Question Answering
Label smoothing is a regularization technique widely used in supervised learning to improve the generalization of models on various tasks, such as image classification and machine translation.
Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next
While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge.
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method.
DORE: Document Ordered Relation Extraction based on Generative Framework
In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models.
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations in the score matrix.
Ranked #2 on
Nested Named Entity Recognition
on GENIA
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue.
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER
Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries.
Towards Collaborative Question Answering: A Preliminary Study
Knowledge and expertise in the real-world can be disjointedly owned.
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
Meanwhile, we utilize an auxiliary response generation task to enhance the model's ability of handling context information, thereby forcing the model to recognize emotions with similar semantics in diverse contexts.
Ranked #5 on
Emotion Recognition in Conversation
on EmoryNLP
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT).
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms.
Aspect-oriented Opinion Extraction
Aspect Sentiment Triplet Extraction
+1
A Unified Generative Framework for Various NER Subtasks
To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework.
Ranked #9 on
Nested Named Entity Recognition
on GENIA
Accelerating BERT Inference for Sequence Labeling via Early-Exit
To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks.
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa
In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree.
Text Information Aggregation with Centrality Attention
A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves aggregation process of combining the representations of all the words, such as pooling or self-attention.
BERT for Monolingual and Cross-Lingual Reverse Dictionary
Reverse dictionary is the task to find the proper target word given the word description.
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP
The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS.
FLAT: Chinese NER Using Flat-Lattice Transformer
Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information.
Ranked #5 on
Chinese Named Entity Recognition
on MSRA
Chinese Named Entity Recognition
named-entity-recognition
+2
Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing.
TENER: Adapting Transformer Encoder for Named Entity Recognition
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Ranked #11 on
Chinese Named Entity Recognition
on Resume NER
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder
Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion.
RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods
This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of positions and orientations of a moving subject from a sequence of IMU sensor measurements.
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing.
Gaussian Word Embedding with a Wasserstein Distance Loss
Therefore, with the aim of representing words in a highly efficient way, we propose to operate a Gaussian word embedding model with a loss function based on the Wasserstein distance.
RIDI: Robust IMU Double Integration
This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone.
Multi-way Particle Swarm Fusion
This paper proposes a novel MAP inference framework for Markov Random Field (MRF) in parallel computing environments.
Turning an Urban Scene Video into a Cinemagraph
Our approach first warps an input video into the viewpoint of a reference camera.
