Search Results for author: Lei Shu

Found 44 papers, 19 papers with code

ODIST: Open World Classification via Distributionally Shifted Instances

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.

Classification Language Modelling

Spatially Adaptive Cloth Regression with Implicit Neural Representations

no code implementations27 Nov 2023 Lei Shu, Vinicius Azevedo, Barbara Solenthaler, Markus Gross

The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.


Fusion-Eval: Integrating Evaluators with LLMs

no code implementations15 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.

Natural Language Understanding

Critique Ability of Large Language Models

no code implementations7 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.

Code Completion Decision Making +2

RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting

no code implementations25 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.

Language Modelling Large Language Model +1

Adapting a Language Model While Preserving its General Knowledge

2 code implementations21 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.

Continual Learning General Knowledge +1

Continual Training of Language Models for Few-Shot Learning

3 code implementations11 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.

Continual Learning Continual Pretraining +2

Open-set Recognition via Augmentation-based Similarity Learning

no code implementations24 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.

Open Set Learning

Zero-Shot Aspect-Based Sentiment Analysis

no code implementations4 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.

Aspect Extraction Natural Language Inference +1

Continual Learning with Knowledge Transfer for Sentiment Classification

2 code implementations18 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.

Classification Continual Learning +4

Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning

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.

Continual Learning Language Modelling +2

CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification 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.

Classification Continual Learning +6

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model

no code implementations4 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.

Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP

1 code implementation6 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

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

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

DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis

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.

Aspect-Based Sentiment Analysis (ABSA) Language Modelling

A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution

1 code implementation4 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.

General Classification Sentiment Analysis +1

Modeling Multi-Action Policy for Task-Oriented Dialogues

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.

Dialogue Management Management

Flexibly-Structured Model for Task-Oriented Dialogues

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.

Task-Oriented Dialogue Systems Text Generation

BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis

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.

Aspect Extraction Sentiment Classification

Review Conversational Reading Comprehension

1 code implementation3 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.

Language Modelling Machine Reading Comprehension

Open-world Learning and Application to Product Classification

1 code implementation17 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.

Classification General Classification +1

Lifelong Domain Word Embedding via Meta-Learning

1 code implementation25 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.

Meta-Learning Word Embeddings

Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

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.

Aspect Extraction

Generative Stock Question Answering

no code implementations21 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.

Question Answering Retrieval

Unseen Class Discovery in Open-world Classification

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.

Classification Clustering +2

Lifelong Word Embedding via Meta-Learning

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.

Meta-Learning Word Embeddings

Product Function Need Recognition via Semi-supervised Attention Network

no code implementations6 Dec 2017 Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu

Functionality is of utmost importance to customers when they purchase products.

Dual Attention Network for Product Compatibility and Function Satisfiability Analysis

no code implementations6 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.


DOC: Deep Open Classification of Text Documents

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.

General Classification Test +2

Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge

no code implementations29 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.

Sentiment Analysis

Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results

no code implementations23 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.

Aspect Extraction Sentiment Analysis

CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews

no code implementations4 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.

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