Search Results for author: Congying Xia

Found 26 papers, 14 papers with code

All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm

no code implementations7 Sep 2023 Jiangshu Du, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu

In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.

Domain Generalization Intent Detection

XGen-7B Technical Report

1 code implementation7 Sep 2023 Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong

Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.

Learning to Select from Multiple Options

1 code implementation1 Dec 2022 Jiangshu Du, Wenpeng Yin, Congying Xia, Philip S. Yu

To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.

Entity Typing Intent Detection +2

Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing

1 code implementation4 Nov 2022 Yibo Wang, Congying Xia, Guan Wang, Philip Yu

In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.

Entity Typing Natural Language Inference

Multifaceted Improvements for Conversational Open-Domain Question Answering

no code implementations1 Apr 2022 TingTing Liang, Yixuan Jiang, Congying Xia, Ziqiang Zhao, Yuyu Yin, Philip S. Yu

Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.

Open-Domain Question Answering Retrieval

Pseudo Siamese Network for Few-shot Intent Generation

no code implementations3 May 2021 Congying Xia, Caiming Xiong, Philip Yu

PSN consists of two identical subnetworks with the same structure but different weights: an action network and an object network.

Intent Detection

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

1 code implementation NAACL 2021 Congying Xia, Wenpeng Yin, Yihao Feng, Philip Yu

Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.

Few-Shot Text Classification General Classification +5

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

1 code implementation COLING 2020 Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.

Decision Making

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

1 code implementation Findings of the Association for Computational Linguistics 2020 Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu

Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.

Few-Shot Learning Generalized Few-Shot Learning +1

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

Joint Training Capsule Network for Cold Start Recommendation

no code implementations23 May 2020 Ting-Ting Liang, Congying Xia, Yuyu Yin, Philip S. Yu

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.

CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection

no code implementations4 Apr 2020 Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, Philip Yu

In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID).

Conditional Text Generation Intent Detection +3

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

no code implementations COLING 2020 Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu

Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.

Entity Typing

Multi-Grained Named Entity Recognition

1 code implementation ACL 2019 Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.

Multi-Grained Named Entity Recognition named-entity-recognition +4

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

no code implementations11 Sep 2018 Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.

Network Embedding Tensor Decomposition

BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder

no code implementations26 Nov 2017 Jiawei Zhang, Congying Xia, Chenwei Zhang, Limeng Cui, Yanjie Fu, Philip S. Yu

The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.

Social and Information Networks Databases

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