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Found 26 papers, 6 papers with code
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
On the depression detection task, our method (F1 = 0. 975~0. 978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0. 760) and architecture engineering (F1 = 0. 756).
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models
Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims.
Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach
Depression is a common disease worldwide.
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
To better mimic human-level conversations that usually fuse various dialog modes, it is essential to build a system that can effectively handle both TOD and ODD and access different knowledge sources.
OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks.
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
Predicting gene functions is a challenge for biologists in the post genomic era.
SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching
We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases.
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding
Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video.
Learning to Rehearse in Long Sequence Memorization
Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
In this paper, we apply one new modality, i. e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario.
M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML
Model Agnostic Meta-Learning~(MAML)~(\cite{finn2017model}) is one of the most well-known gradient-based meta learning algorithms, that learns the meta-initialization through the inner and outer optimization loop.
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e. g. answer type or the answer itself).
Continual Memory: Can We Reason After Long-Term Memorization?
Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning.
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems
For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities or domains.
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Thus, these methods fail to distinguish the target moment from plausible negative moments.
Weakly-Supervised Video Moment Retrieval via Semantic Completion Network
Video moment retrieval is to search the moment that is most relevant to the given natural language query.
Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks
Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context.
Localizing Unseen Activities in Video via Image Query
Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization.
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query.
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness
With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular.
A Statistical Learning Based System for Fake Website Detection
Existing fake website detection systems are unable to effectively detect fake websites.
