103 citations · 244 across the 12 of their papers we have counts for
12 papers
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…
On pseudo-Anosov autoequivalences
Yu-Wei Fan, Simion Filip, Fabian Haiden +2
Motivated by results of Thurston, we prove that any autoequivalence of a triangulated category induces a filtration by triangulated subcategories, provided the existence of Bridgel…
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…
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…
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…
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…
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…
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
Yutai Hou, Yijia Liu, Wanxiang Che +1
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance witho…
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
Haoyang Wen, Yijia Liu, Wanxiang Che +2
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. Co…
Distilling Knowledge for Search-based Structured Prediction
Yijia Liu, Wanxiang Che, Huaipeng Zhao +2
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained wi…
Parsing Tweets into Universal Dependencies
Yijia Liu, Yi Zhu, Wanxiang Che +3
We study the problem of analyzing tweets with Universal Dependencies. We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech…
Exploring Segment Representations for Neural Segmentation Models
Yijia Liu, Wanxiang Che, Jiang Guo +2
Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. O…