Search Results for author:
Found 20 papers, 13 papers with code
Implicit Generative Prior for Bayesian Neural Networks
The results of our experiments highlight the superiority of our proposed framework over existing methods, such as sparse variational Bayesian and generative models, in terms of prediction accuracy and uncertainty quantification.
Improving Biomedical Pretrained Language Models with Knowledge
To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.
Ranked #1 on
Named Entity Recognition (NER)
on JNLPBA
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations.
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation
Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
In this paper, we explore the slot tagging with only a few labeled support sentences (a. k. a.
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.
Ranked #1 on
UCCA Parsing
on CoNLL 2019
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system.
Ranked #6 on
Task-Oriented Dialogue Systems
on KVRET
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages.
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Ranked #2 on
AMR Parsing
on LDC2014T12:
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on
Dependency Parsing
on Universal Dependencies
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base.
Ranked #7 on
Task-Oriented Dialogue Systems
on KVRET
Distilling Knowledge for Search-based Structured Prediction
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
Parsing Tweets into Universal Dependencies
Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD.
Ranked #2 on
Dependency Parsing
on Tweebank
Exploring Segment Representations for Neural Segmentation Models
Many natural language processing (NLP) tasks can be generalized into segmentation problem.
