Search Results for author:
Found 39 papers, 11 papers with code
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.
Towards Generalising Neural Topical Representations
Although NTMs have achieved promising performance when trained and tested on a specific corpus, their generalisation ability across corpora is rarely studied.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process.
Multimodal Neural Processes for Uncertainty Estimation
For the first time, we propose a new model of NP family for multimodal uncertainty estimation, namely Multimodal Neural Processes.
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing
Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses.
Ranked #1 on
Speech Emotion Recognition
on LSSED
HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing
Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.
AUC Maximization for Low-Resource Named Entity Recognition
To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Domain adaptation is an effective solution to data scarcity in low-resource scenarios.
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Uncertainty estimation is essential to make neural networks trustworthy in real-world applications.
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
We convert the ELR framework to estimate the increase in (strictly proper) scores like log probability or negative mean square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS).
Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation.
Neural Attention-Aware Hierarchical Topic Model
Neural topic models (NTMs) apply deep neural networks to topic modelling.
Leveraging Information Bottleneck for Scientific Document Summarization
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.
Prototype-Guided Memory Replay for Continual Learning
Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge.
Learning Graph Neural Networks with Positive and Unlabeled Nodes
By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs.
Stratified Sampling for Extreme Multi-Label Data
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data.
Topic Modelling Meets Deep Neural Networks: A Survey
Topic modelling has been a successful technique for text analysis for almost twenty years.
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer
However, most of the existing knowledge distillation methods consider only one type of knowledge learned from either instance features or instance relations via a specific distillation strategy in teacher-student learning.
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.
Leveraging Meta Information in Short Text Aggregation
Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models.
Variational Autoencoders for Sparse and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures.
Improving Topic Models with Latent Feature Word Representations
Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks.
Inter and Intra Topic Structure Learning with Word Embeddings
One important task of topic modeling for text analysis is interpretability.
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
Besides the text content, documents and their associated words usually come with rich sets of meta informa- tion, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings.
Unsupervised Text Segmentation Based on Native Language Characteristics
Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language.
Leveraging Node Attributes for Incomplete Relational Data
Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction.
Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling).
