Search Results for author:
Found 44 papers, 19 papers with code
ODIST: Open World Classification via Distributionally Shifted Instances
In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances.
Spatially Adaptive Cloth Regression with Implicit Neural Representations
The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.
Fusion-Eval: Integrating Evaluators with LLMs
Evaluating Large Language Models (LLMs) is a complex task, especially considering the intricacies of natural language understanding and the expectations for high-level reasoning.
SiRA: Sparse Mixture of Low Rank Adaptation
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks.
Critique Ability of Large Language Models
In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.
Towards an On-device Agent for Text Rewriting
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting.
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting
Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities in long-form text generation tasks expressed through natural language instructions.
Adapting a Language Model While Preserving its General Knowledge
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 Training of Language Models for Few-Shot Learning
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Ranked #1 on
Continual Pretraining
on AG News
Open-set Recognition via Augmentation-based Similarity Learning
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.
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
First, we measure and analyze model update regression in different model update settings.
Zero-Shot Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning.
Continual Learning with Knowledge Transfer for Sentiment Classification
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)
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
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)
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
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.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model
Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
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
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
Controllable Text Generation with Focused Variation
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
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.
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
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.
Modeling Multi-Action Policy for Task-Oriented Dialogues
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
Flexibly-Structured Model for Task-Oriented Dialogues
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.
Controlled CNN-based Sequence Labeling for Aspect Extraction
The modified CNN has two types of control modules.
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
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.
Review Conversational Reading Comprehension
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.
Open-world Learning and Application to Product Classification
Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training.
Lifelong Domain Word Embedding via Meta-Learning
Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks.
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
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.
Generative Stock Question Answering
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.
Unseen Class Discovery in Open-world Classification
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.
Lifelong Word Embedding via Meta-Learning
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.
Product Function Need Recognition via Semi-supervised Attention Network
Functionality is of utmost importance to customers when they purchase products.
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
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
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.
Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge
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.
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction.
Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results
When "screen" appears in a review of a new domain (or product), it is likely to be an aspect too.
Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion
In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA.
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
One important product feature is the complementary entity (products) that may potentially work together with the reviewed product.
