no code implementations • Findings (EMNLP) 2021 • Lei Shu, Yassine Benajiba, Saab Mansour, Yi Zhang
In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances.
no code implementations • 27 Nov 2023 • Lei Shu, Vinicius Azevedo, Barbara Solenthaler, Markus Gross
The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.
no code implementations • 15 Nov 2023 • Lei Shu, Nevan Wichers, Liangchen Luo, Yun Zhu, Yinxiao Liu, Jindong Chen, Lei Meng
Evaluating Large Language Models (LLMs) is a complex task, especially considering the intricacies of natural language understanding and the expectations for high-level reasoning.
no code implementations • 15 Nov 2023 • Yun Zhu, Nevan Wichers, Chu-Cheng Lin, Xinyi Wang, Tianlong Chen, Lei Shu, Han Lu, Canoee Liu, Liangchen Luo, Jindong Chen, Lei Meng
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks.
no code implementations • 7 Oct 2023 • Liangchen Luo, Zi Lin, Yinxiao Liu, Lei Shu, Yun Zhu, Jingbo Shang, Lei Meng
In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.
no code implementations • 22 Aug 2023 • Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting.
no code implementations • 25 May 2023 • Lei Shu, Liangchen Luo, Jayakumar Hoskere, Yun Zhu, Canoee Liu, Simon Tong, Jindong Chen, Lei Meng
Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities in long-form text generation tasks expressed through natural language instructions.
2 code implementations • 21 Jan 2023 • Zixuan Ke, Yijia Shao, Haowei Lin, Hu Xu, Lei Shu, Bing Liu
This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.
3 code implementations • 11 Oct 2022 • Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, Bing Liu
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Ranked #1 on
Continual Pretraining
on AG News
no code implementations • 24 Mar 2022 • Sepideh Esmaeilpour, Lei Shu, Bing Liu
In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected.
no code implementations • 7 Feb 2022 • Deng Cai, Elman Mansimov, Yi-An Lai, Yixuan Su, Lei Shu, Yi Zhang
First, we measure and analyze model update regression in different model update settings.
no code implementations • 4 Feb 2022 • Lei Shu, Hu Xu, Bing Liu, Jiahua Chen
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning.
2 code implementations • 18 Dec 2021 • Zixuan Ke, Bing Liu, Hao Wang, Lei Shu
In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain.
Ranked #4 on
Continual Learning
on DSC (10 tasks)
1 code implementation • NeurIPS 2021 • Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, Lei Shu
Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge.
Ranked #1 on
Continual Learning
on DSC (10 tasks)
1 code implementation • EMNLP 2021 • Zixuan Ke, Bing Liu, Hu Xu, Lei Shu
The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing.
2 code implementations • Findings (NAACL) 2022 • Yixuan Su, Fangyu Liu, Zaiqiao Meng, Tian Lan, Lei Shu, Ehsan Shareghi, Nigel Collier
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
no code implementations • 4 Nov 2021 • Chao Qi, Junfeng Gao, Simon Pearson, Helen Harman, Kunjie Chen, Lei Shu
Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development.
2 code implementations • ACL 2022 • Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, Yi Zhang
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
1 code implementation • 6 Sep 2021 • Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
2 code implementations • COLING 2020 • Hu Xu, Lei Shu, Philip S. Yu, Bing Liu
Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.
Aspect-Based Sentiment Analysis (ABSA)
Language Modelling
+1
no code implementations • Findings of the Association for Computational Linguistics 2020 • Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding.
1 code implementation • 4 Nov 2019 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence.
1 code implementation • IJCNLP 2019 • Lei Shu, Hu Xu, Bing Liu, Piero Molino
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
1 code implementation • WS 2019 • Lei Shu, Piero Molino, Mahdi Namazifar, Hu Xu, Bing Liu, Huaixiu Zheng, Gokhan Tur
It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.
no code implementations • 15 May 2019 • Lei Shu, Hu Xu, Bing Liu
The modified CNN has two types of control modules.
1 code implementation • NAACL 2019 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.
1 code implementation • 3 Feb 2019 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses.
1 code implementation • 17 Sep 2018 • Hu Xu, Bing Liu, Lei Shu, P. Yu
Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training.
1 code implementation • 25 May 2018 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks.
2 code implementations • ACL 2018 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings.
no code implementations • 21 Apr 2018 • Zhaopeng Tu, Yong Jiang, Xiaojiang Liu, Lei Shu, Shuming Shi
We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing action recommendations to stocks upon user's requests.
1 code implementation • ICLR 2018 • Lei Shu, Hu Xu, Bing Liu
It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them.
no code implementations • ICLR 2018 • Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.
no code implementations • 6 Dec 2017 • Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
Functionality is of utmost importance to customers when they purchase products.
no code implementations • 6 Dec 2017 • Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products.
no code implementations • EMNLP 2017 • Lei Shu, Hu Xu, Bing Liu
As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem.
no code implementations • 29 May 2017 • Hu Xu, Lei Shu, Philip S. Yu
Extracting opinion targets is an important task in sentiment analysis on product reviews and complementary entities (products) are one important type of opinion targets that may work together with the reviewed product.
no code implementations • ACL 2017 • Lei Shu, Hu Xu, Bing Liu
This paper makes a focused contribution to supervised aspect extraction.
no code implementations • 23 Dec 2016 • Lei Shu, Bing Liu, Hu Xu, Annice Kim
When "screen" appears in a review of a new domain (or product), it is likely to be an aspect too.
no code implementations • 14 Dec 2016 • Hu Xu, Lei Shu, Jingyuan Zhang, Philip S. Yu
In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA.
no code implementations • 4 Dec 2016 • Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
One important product feature is the complementary entity (products) that may potentially work together with the reviewed product.