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Found 21 papers, 2 papers with code
Semantic Novelty Detection in Natural Language Descriptions
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization
In this work, we develop a prompting approach for incremental summarization of task videos.
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
Lifelong and Continual Learning Dialogue Systems
This book introduces the new paradigm of lifelong learning dialogue systems to endow chatbots the ability to learn continually by themselves through their own self-initiated interactions with their users and working environments to improve themselves.
Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities
Much of the existing work on text novelty detection has been studied at the topic level, i. e., identifying whether the topic of a document or a sentence is novel or not.
Knowledge-Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP).
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances.
Self-Initiated Open World Learning for Autonomous AI Agents
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and self-supervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
FLIN: A Flexible Natural Language Interface for Web Navigation
AI assistants can now carry out tasks for users by directly interacting with website UIs.
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
Lifelong Learning Dialogue Systems: Chatbots that Self-Learn On the Job
Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification
This paper studies the task of comparative preference classification (CPC).
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
We refer to this problem as domain polarity-changes of words.
Building an Application Independent Natural Language Interface
Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.
Lifelong and Interactive Learning of Factual Knowledge in Dialogues
Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses.
Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of \textit{state-action permissibility} (SAP).
Target-Sensitive Memory Networks for Aspect Sentiment Classification
In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target.
Towards a Continuous Knowledge Learning Engine for Chatbots
We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it.
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
Stage one extracts/groups the target-related words (call t-words) for a given target.
Context-aware Path Ranking for Knowledge Base Completion
PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction.
