360 citations · 690 across the 24 of their papers we have counts for
25 papers
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting
Lei Shu, Liangchen Luo, Jayakumar Hoskere +5
Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final…
Adapting a Language Model While Preserving its General Knowledge
Zixuan Ke, Yijia Shao, Haowei Lin +3
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a…
Continual Training of Language Models for Few-Shot Learning
Zixuan Ke, Haowei Lin, Yijia Shao +3
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can pr…
Zero-Shot Aspect-Based Sentiment Analysis
Lei Shu, Hu Xu, Bing Liu +1
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new…
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
Deng Cai, Elman Mansimov, Yi-An Lai +3
Recent advance in deep learning has led to the rapid adoption of machine learning-based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backwar…
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
Hu Xu, Lei Shu, Philip S. Yu +1
This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent pro…
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Hu Xu, Bing Liu, Lei Shu +1
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 classif…
Modeling Multi-Action Policy for Task-Oriented Dialogues
Lei Shu, Hu Xu, Bing Liu +1
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only o…
Flexibly-Structured Model for Task-Oriented Dialogues
Lei Shu, Piero Molino, Mahdi Namazifar +4
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, wher…
Controlled CNN-based Sequence Labeling for Aspect Extraction
Lei Shu, Hu Xu, Bing Liu
One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extracti…
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Hu Xu, Bing Liu, Lei Shu +1
Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase dec…
Review Conversational Reading Comprehension
Hu Xu, Bing Liu, Lei Shu +1
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 fro…
Open-world Learning and Application to Product Classification
Hu Xu, Bing Liu, Lei Shu +1
Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen cl…
Lifelong Domain Word Embedding via Meta-Learning
Hu Xu, Bing Liu, Lei Shu +1
Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks. General-purpose embeddings trained on large-scale corpora are often sub-…
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
Hu Xu, Bing Liu, Lei Shu +1
One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised…
Generative Stock Question Answering
Zhaopeng Tu, Yong Jiang, Xiaojiang Liu +2
We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing act…
Unseen Class Discovery in Open-world Classification
Lei Shu, Hu Xu, Bing Liu
This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject exampl…
Product Function Need Recognition via Semi-supervised Attention Network
Hu Xu, Sihong Xie, Lei Shu +1
Functionality is of utmost importance to customers when they purchase products. However, it is unclear to customers whether a product can really satisfy their needs on functions. F…
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
Hu Xu, Sihong Xie, Lei Shu +1
Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products. Due to th…
DOC: Deep Open Classification of Text Documents
Lei Shu, Hu Xu, Bing Liu
Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or t…
Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge
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 wo…
Lifelong Learning CRF for Supervised Aspect Extraction
Lei Shu, Hu Xu, Bing Liu
This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their resul…
Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results
Lei Shu, Bing Liu, Hu Xu +1
One of the key tasks of sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. In this work, we focus on using super…
Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion
Hu Xu, Lei Shu, Jingyuan Zhang +1
Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and revie…