Search Results for author:
Found 33 papers, 12 papers with code
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets.
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space
Previous studies attempted to introduce discrete or Gaussian-based continuous latent variables to address the one-to-many problem, but the diversity is limited.
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation
To evaluate the efficacy of data augmentation methods for open-domain dialogue, we designed a clustering-based metric to characterize the semantic diversity of the augmented dialogue data.
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
Make a Choice! Knowledge Base Question Answering with In-Context Learning
Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB).
CED: Catalog Extraction from Documents
To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task.
Ranked #1 on
Catalog Extraction
on ChCatExt
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks.
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model.
A Method of Query Graph Reranking for Knowledge Base Question Answering
This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA).
Better Query Graph Selection for Knowledge Base Question Answering
This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA).
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
Exploiting Rich Syntax for Better Knowledge Base Question Answering
Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees. In this paper, we propose an approach to learn syntax-based representations for KBQA.
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Improving Neural Relation Extraction with Positive and Unlabeled Learning
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
IPRE: a Dataset for Inter-Personal Relationship Extraction
Our data is the first dataset for inter-personal relationship extraction.
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.
Chinese Named Entity Recognition
named-entity-recognition
+5
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
Distributed Representations for Building Profiles of Users and Items from Text Reviews
In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.
