Search Results for author:
Found 46 papers, 30 papers with code
Preserving Cross-Linguality of Pre-trained Models via Continual Learning
Recently, fine-tuning pre-trained language models (e. g., multilingual BERT) to downstream cross-lingual tasks has shown promising results.
CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which.
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
We apply SNP2Vec to perform long-sequence genomics modeling, and we evaluate the effectiveness of our approach on predicting Alzheimer's disease risk in a Chinese cohort.
Multi-Stage Prompting for Knowledgeable Dialogue Generation
We propose a multi-stage prompting approach to generate knowledgeable responses from a single pretrained LM.
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation
ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong.
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging
Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains.
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Mixup is a popular data-dependent augmentation technique for deep neural networks, which contains two sub-tasks, mixup generation and classification.
Ranked #5 on
Image Classification
on Tiny ImageNet Classification
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model.
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer
We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other.
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet.
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which.
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains.
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task.
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation
The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems.
Unveiling the Power of Mixup for Stronger Classifiers
Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i. e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework.
Ranked #6 on
Image Classification
on Tiny ImageNet Classification
Are Multilingual Models Effective in Code-Switching?
Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks.
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available.
Multimodal End-to-End Sparse Model for Emotion Recognition
Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single modality with hand-crafted algorithms and then performing end-to-end learning with the extracted features.
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection
Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task.
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment
Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal.
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems
In this paper, we propose a method to embed the KB, of any size, directly into the model parameters.
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition
Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between different emotion categories are not utilized, which leads to sub-optimal performance; and 2) current models fail to cope well with low-resource emotions, especially for unseen emotions.
EmoGraph: Capturing Emotion Correlations using Graph Networks
Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnections among them.
Ranked #3 on
Emotion Classification
on SemEval 2018 Task 1E-c
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems
In this paper, we evaluate the priming few-shot ability of language models in the NLU, DST, DP and NLG tasks.
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning
Recently, fine-tuning pre-trained language models (e. g., multilingual BERT) to downstream cross-lingual tasks has shown promising results.
Meta-Transfer Learning for Code-Switched Speech Recognition
An increasing number of people in the world today speak a mixed-language as a result of being multilingual.
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection
Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task.
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling.
Cross-Domain Named Entity Recognition
named-entity-recognition
+1
Variational Transformers for Diverse Response Generation
Despite the great promise of Transformers in many sequence modeling tasks (e. g., machine translation), their deterministic nature hinders them from generalizing to high entropy tasks such as dialogue response generation.
XPersona: Evaluating Multilingual Personalized Chatbot
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.
Learning Fast Adaptation on Cross-Accented Speech Recognition
The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system.
Audio and Speech Processing Sound
Zero-Resource Cross-Domain Named Entity Recognition
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.
Ranked #1 on
Cross-Domain Named Entity Recognition
on CoNLL04
On the Importance of Word Order Information in Cross-lingual Sequence Labeling
To verify this hypothesis, we investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs).
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English.
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Despite the surging demands for multilingual task-oriented dialog systems (e. g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios.
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning
With a large number of datasets being released and new techniques being proposed, Question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC)tasks.
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer
Highly performing deep neural networks come at the cost of computational complexity that limits their practicality for deployment on portable devices.
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition
In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching.
Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos
Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF.
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
This paper describes CAiRE's submission to the unsupervised machine translation track of the WMT'19 news shared task from German to Czech.
CAiRE: An Empathetic Neural Chatbot
In this paper, we present an end-to-end empathetic conversation agent CAiRE.
Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data
This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection.
