activity
20162020
most citedFew-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

7 citations · 7 across the 1 of their papers we have counts for

collaborators

11 papers

cs.CL20207 cited

Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

Yutai Hou, Wanxiang Che, Yongkui Lai +4

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-sho…

cs.CL2019

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever

Libo Qin, Yijia Liu, Wanxiang Che +3

Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats…

cs.CL2019

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing

Yuxuan Wang, Wanxiang Che, Jiang Guo +2

This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient app…

cs.CL2019

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

Yutai Hou, Zhihan Zhou, Yijia Liu +4

While few-shot classification has been widely explored with similarity based methods, few-shot sequence labeling poses a unique challenge as it also calls for modeling the label de…

cs.CL2018

An AMR Aligner Tuned by Transition-based Parser

Yijia Liu, Wanxiang Che, Bo Zheng +2

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. Our…

cs.CL2018

Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation

Wanxiang Che, Yijia Liu, Yuxuan Wang +2

This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanfo…