146 citations · 228 across the 21 of their papers we have counts for
21 papers
Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings
Minsik Oh, Jiwei Li, Guoyin Wang
Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost.…
Text Classification via Large Language Models
Xiaofei Sun, Xiaoya Li, Jiwei Li +4
Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classi…
Towards Building the Federated GPT: Federated Instruction Tuning
Jianyi Zhang, Saeed Vahidian, Martin Kuo +6
While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amou…
GPT-NER: Named Entity Recognition via Large Language Models
Shuhe Wang, Xiaofei Sun, Xiaoya Li +5
Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised ba…
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
Jiang Xie, Shuyin Xia, Guoyin Wang +1
Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is us…
A novel cluster internal evaluation index based on hyper-balls
Jiang Xie, Pengfei Zhao, Shuyin Xia +2
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is car…
Granular-Ball Fuzzy Set and Its Implementation in SVM
Shuyin Xia, Xiaoyu Lian, Guoyin Wang +2
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing. Consequently, these methods are neither effic…
Learning Personalized Representations using Graph Convolutional Network
Hongyu Shen, Jinoh Oh, Shuai Zhao +3
Generating representations that precisely reflect customers' behavior is an important task for providing personalized skill routing experience in Alexa. Currently, Dynamic Routing…
Improving Downstream Task Performance by Treating Numbers as Entities
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +2
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not a…
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
Shuyin Xia, Cheng Wang, Guoyin Wang +5
Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlak can use equivalence classes to represent knowledge, but it cannot process c…
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Zihan Yan, Li Liu, Xin Li +4
Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximit…
Rethinking the Image Feature Biases Exhibited by Deep CNN Models
Dawei Dai, Yutang Li, Huanan Bao +3
In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, such deep neural models are still regarded as black box in most tasks.…
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li +6
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural netw…
Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding
Joo-Kyung Kim, Guoyin Wang, Sungjin Lee +1
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When am…
AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
Xinnuo Xu, Guoyin Wang, Young-Bum Kim +1
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For larg…
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Shuyin Xia, Wenhua Li, Guoyin Wang +3
In this paper, we propose and prove the theorem regarding the stability of attributes in a decision system. Based on the theorem, we propose the LRA framework for accelerating roug…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Weakly supervised cross-domain alignment with optimal transport
Siyang Yuan, Ke Bai, Liqun Chen +6
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural langu…
Improving Adversarial Text Generation by Modeling the Distant Future
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words…
Ball k-means
Shuyin Xia, Daowan Peng, Deyu Meng +4
This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid…
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Yizhe Zhang, Guoyin Wang, Chunyuan Li +3
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation. However, the…