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Found 36 papers, 15 papers with code
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models
Enhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications.
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition
In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.
Learning VAE-LDA Models with Rounded Reparameterization Trick
The introduction of VAE provides an efficient framework for the learning of generative models, including generative topic models.
Keyphrase Extraction with Incomplete Annotated Training Data
Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing.
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking
As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
The Composed Image Retrieval (CIR) task aims to retrieve target images using a composed query consisting of a reference image and a modified text.
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the Gradient Dissipation, the Weight, and the Ratio.
Anaphor Assisted Document-Level Relation Extraction
Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities.
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
An intriguing phenomenon in CSE is the significant performance gap between supervised and unsupervised methods, with their only difference lying in the training data.
Code-Style In-Context Learning for Knowledge-Based Question Answering
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
Referential dialogue is a superset of various vision-language (VL) tasks.
Ranked #10 on
Visual Question Answering
on ViP-Bench
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training.
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes.
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance.
Cross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning.
cross-domain few-shot learning
Few-Shot Relation Classification
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings.
Ranked #1 on
Subjectivity Analysis
on SUBJ
Robust Regularization with Adversarial Labelling of Perturbed Samples
The ALPS regularization objective is formulated as a min-max problem, in which the outer problem is minimizing an upper-bound of the VRM loss, and the inner problem is L$_1$-ball constrained adversarial labelling on perturbed sample.
Regularizing Neural Networks via Adversarial Model Perturbation
This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better.
Ranked #8 on
Image Classification
on SVHN
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Neural Dialogue State Tracking with Temporally Expressive Networks
Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue.
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models.
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.
Ranked #8 on
Relation Extraction
on WebNLG
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
We show that IB learning is, in fact, equivalent to a special class of the quantization problem.
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence.
MixUp as Directional Adversarial Training
We prove that the family of Untied MixUp schemes is equivalent to the entire class of DAT schemes.
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification.
Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks
Gated models resolve this conflict by adaptively adjusting their state-update equations, whereas Vanilla RNN resolves this conflict by assigning different dimensions different tasks.
Syntax Encoding with Application in Authorship Attribution
We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation.
MixUp as Locally Linear Out-Of-Manifold Regularization
To address this issue, we propose a novel adaptive version of MixUp, where the mixing policies are automatically learned from the data using an additional network and objective function designed to avoid manifold intrusion.
The APVA-TURBO Approach To Question Answering in Knowledge Base
In this paper, we study the problem of question answering over knowledge base.
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization
Based on the notion of information bottleneck (IB), we formulate a quantization problem called "IB quantization".
Prototypical Recurrent Unit
For this purpose, we design a simple and controllable task, called ``memorization problem'', where the networks are trained to memorize certain targeted information.
On the representation and embedding of knowledge bases beyond binary relations
The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary.
