Search Results for author:
Found 37 papers, 10 papers with code
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette.
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition
With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities.
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability.
Adversarial Imitation Learning from Incomplete Demonstrations
In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a policy from demonstrations with incomplete action sequences, i. e., incomplete demonstrations.
VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking
The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech.
CASS: Towards Building a Social-Support Chatbot for Online Health Community
Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community.
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.
Ridesourcing Car Detection by Transfer Learning
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.
SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition
This paper explores the role of semantic relatedness features, such as word associations, in humour recognition.
Predicting Word Association Strengths
This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973).
Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games
This paper explores humour recognition for Twitter-based hashtag games.
Recognizing Humour using Word Associations and Humour Anchor Extraction
This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models.
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach
In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample.
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition
We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition.
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization
Physical common sense plays an essential role in the cognition abilities of robots for human-robot interaction.
Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites
Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems.
Optimizing Fund Allocation for Game-based Verifiable Computation Outsourcing
The cloud server makes a deposit for each task it takes, each client allocates a budget (including the wage for the server and the cost for possibly hiring TTP) for each task submitted, and every party has its limited fund for either deposits or task budgets.
Computer Science and Game Theory
Inspecting the Process of Bank Credit Rating via Visual Analytics
We propose RatingVis to assist experts in exploring and comparing different bank credit rating schemes.
Human-Centered AI for Data Science: A Systematic Approach
Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control.
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games
In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore.
TalkTive: A Conversational Agent Using Backchannels to Engage Older Adults in Neurocognitive Disorders Screening
This is used in the development of TalkTive, a CA which can predict both timing and form of backchanneling during cognitive assessments.
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Creating presentation slides is a critical but time-consuming task for data scientists.
Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion
In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts.
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level.
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers.
Storyfier: Exploring Vocabulary Learning Support with Text Generation Models
Vocabulary learning support tools have widely exploited existing materials, e. g., stories or video clips, as contexts to help users memorize each target word.
ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions
Unfamiliar decisions -- decisions where people lack adequate domain knowledge or expertise -- specifically increase the complexity and uncertainty of the process of searching for, understanding, and making decisions with online information.
TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation
Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility.
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL).
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct.
FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation
Preference-based learning aims to align robot task objectives with human values.
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole.
