Search Results for author:
Found 139 papers, 60 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.
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach
Resolving dependencies among dialogue history is one of the main obstacles in the research on conversational question answering (QA).
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
Dimsum @LaySumm 20
Lay summarization aims to generate lay summaries of scientific papers automatically.
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.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
In this work, we focus on developing resources for languages in Indonesia.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
AiSocrates: Towards Answering Ethical Quandary Questions
We show that AiSocrates generates promising answers to ethical quandary questions with multiple perspectives, 6. 92% more often than answers written by human philosophers by one measure, but the system still needs improvement to match the coherence of human philosophers fully.
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.
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Based on our discovery that title provides a good signal for framing bias, we present NeuS-TITLE that learns to neutralize news content in hierarchical order from title to article.
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task.
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation
Furthermore, the original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks.
VScript: Controllable Script Generation with Audio-Visual Presentation
Automatic script generation could save a considerable amount of resources and offer inspiration to professional scriptwriters.
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question.
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition
With the rise of deep learning and intelligent vehicle, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities.
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset
We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset.
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.
Confucius, Cyberpunk and Mr. Science: Comparing AI ethics between China and the EU
Even when people from different cultures happen to agree on a set of common principles, it does not necessarily mean that they share the same understanding of these concepts and what they entail.
Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning.
Language Models are Few-shot Multilingual Learners
General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples.
Greenformer: Factorization Toolkit for Efficient Deep Neural Networks
While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
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.
Assessing Political Prudence of Open-domain Chatbots
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
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.
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.
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions.
ERICA: An Empathetic Android Companion for Covid-19 Quarantine
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems.
Nora: The Well-Being Coach
The current pandemic has forced people globally to remain in isolation and practice social distancing, which creates the need for a system to combat the resulting loneliness and negative emotions.
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query.
QAConv: Question Answering on Informative Conversations
This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source.
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.
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition
In this paper, we propose to leverage these datasets using weakly-supervised multi-task learning to improve the generalization performance on each of them.
Dynamically Addressing Unseen Rumor via Continual Learning
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model.
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems.
On Unifying Misinformation Detection
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup.
Mitigating Media Bias through Neutral Article Generation
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing.
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.
Towards Few-Shot Fact-Checking via Perplexity
Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models.
Model Generalization on COVID-19 Fake News Detection
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.
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.
A Study on the Autoregressive and non-Autoregressive Multi-label Learning
These tasks are hard because the label space is usually (i) very large, e. g. thousands or millions of labels, (ii) very sparse, i. e. very few labels apply to each input document, and (iii) highly correlated, meaning that the existence of one label changes the likelihood of predicting all other labels.
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.
Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
Lay summarization aims to generate lay summaries of scientific papers automatically.
Multi-hop Question Generation with Graph Convolutional Network
Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs.
Plug-and-Play Conversational Models
These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models
We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process.
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.
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data.
Ranked #13 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+3
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.
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in the natural language processing (NLP) is slow-moving due to a lack of available resources.
Natural Language Processing
Natural Language Understanding
+1
The Adapter-Bot: All-In-One Controllable Conversational Model
The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.
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.
Misinformation Has High Perplexity
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly.
Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing.
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management
We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts.
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
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results.
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.
Attention over Parameters for Dialogue Systems
Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans.
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.
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences
Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure.
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Sensational headlines are headlines that capture people's attention and generate reader interest.
On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression
Despite their ubiquity in NLP tasks, Long Short-Term Memory (LSTM) networks suffer from computational inefficiencies caused by inherent unparallelizable recurrences, which further aggravates as LSTMs require more parameters for larger memory capacity.
MoEL: Mixture of Empathetic Listeners
Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions.
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.
Getting To Know You: User Attribute Extraction from Dialogues
User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated.
Understanding the Shades of Sexism in Popular TV Series
[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series.
Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition
In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings.
Exploring Social Bias in Chatbots using Stereotype Knowledge
Exploring social bias in chatbot is an important, yet relatively unexplored problem.
CAiRE: An Empathetic Neural Chatbot
In this paper, we present an end-to-end empathetic conversation agent CAiRE.
Generating Empathetic Responses by Looking Ahead the User's Sentiment
Hence, in this paper, we propose Sentiment Look-ahead, which is a novel perspective for empathy that models the future user emotional state.
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed.
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems
We can then solve efficiently the problem of multi-label learning with an intractably large number of interdependent labels, such as automatic tagging of Wikipedia pages.
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed.
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.
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
A novel repetition normalized adversarial reward for headline generation
While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases \cite{paulus2017deep}.
Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets
It outperforms a similar CNN using spectrograms as input by 12. 8% for emotion and 6. 3% for personality, based on F-scores.
Modality-based Factorization for Multimodal Fusion
We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference tasks.
GlobalTrait: Personality Alignment of Multilingual Word Embeddings
We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian.
Towards End-to-end Automatic Code-Switching Speech Recognition
Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as "one" in English and "w\`an" in Chinese.
Learning Comment Generation by Leveraging User-Generated Data
Existing models on open-domain comment generation are difficult to train, and they produce repetitive and uninteresting responses.
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
Building large-scale datasets for training code-switching language models is challenging and very expensive.
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases.
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors.
Ranked #25 on
Sentiment Analysis
on SST-5 Fine-grained classification
Reducing Gender Bias in Abusive Language Detection
In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures.
Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction
Furthermore, we can see the prediction relevance of each modality for each trait.
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags
This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks.
Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision
The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74. 1%) and f-score (74. 3%).
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition
We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data.
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
Lack of text data has been the major issue on code-switching language modeling.
Investigating Audio, Visual, and Text Fusion Methods for End-to-End Automatic Personality Prediction
Furthermore, we can see the prediction relevance of each modality for each trait.
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks.
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases.
Ranked #10 on
Task-Oriented Dialogue Systems
on KVRET
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.
One-step and Two-step Classification for Abusive Language Detection on Twitter
Automatic abusive language detection is a difficult but important task for online social media.
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition
Zara, or {`}Zara the Supergirl{'} is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module.
Towards Empathetic Human-Robot Interactions
Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can "understand" human emotion.
Deep Learning of Audio and Language Features for Humor Prediction
Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.
A Machine Learning based Music Retrieval and Recommendation System
In this paper, we present a music retrieval and recommendation system using machine learning techniques.
Co-Training for Classification of Live or Studio Music Recordings
The fast-spreading development of online streaming services has enabled people from all over the world to listen to music.
A Hindi-English Code-Switching Corpus
The aim of this paper is to investigate the rules and constraints of code-switching (CS) in Hindi-English mixed language data.
A Multilingual Natural Stress Emotion Database
In this paper, we describe an ongoing effort in collecting and annotating a multilingual speech database of natural stress emotion from university students.
A Mandarin-English Code-Switching Corpus
Generally the existing monolingual corpora are not suitable for large vocabulary continuous speech recognition (LVCSR) of code-switching speech.
