9 research outputs found
Context-aware Path Ranking for Knowledge Base Completion
Knowledge base (KB) completion aims to infer missing facts from existing ones
in a KB. Among various approaches, path ranking (PR) algorithms have received
increasing attention in recent years. PR algorithms enumerate paths between
entity pairs in a KB and use those paths as features to train a model for
missing fact prediction. Due to their good performances and high model
interpretability, several methods have been proposed. However, most existing
methods suffer from scalability (high RAM consumption) and feature explosion
(trains on an exponentially large number of features) problems. This paper
proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems
by introducing a selective path exploration strategy. C-PR learns global
semantics of entities in the KB using word embedding and leverages the
knowledge of entity semantics to enumerate contextually relevant paths using
bidirectional random walk. Experimental results on three large KBs show that
the path features (fewer in number) discovered by C-PR not only improve
predictive performance but also are more interpretable than existing baselines
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. However, as the KBs are
inherently incomplete and remain fixed during conversation, it limits dialogue
systems' ability to answer questions and to handle questions involving entities
or relations that are not in the KB. In this paper, we make an attempt to
propose an engine for Continuous and Interactive Learning of Knowledge (CILK)
for dialogue systems to give them the ability to continuously and interactively
learn and infer new knowledge during conversations. With more knowledge
accumulated over time, they will be able to learn better and answer more
questions. Our empirical evaluation shows that CILK is promising.Comment: Published in SIGDIAL 201
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. However, the task is challenging as
one often needs to consider domain-specific knowledge (e.g., iPad is a tablet
and has aspect resolution) to identify coreferences in opinionated reviews.
Also, compiling a handcrafted and curated domain-specific knowledge base for
each domain is very time consuming and arduous. This paper proposes an approach
to automatically mine and leverage domain-specific knowledge for classifying
objects and attribute coreferences. The approach extracts domain-specific
knowledge from unlabeled review data and trains a knowledgeaware neural
coreference classification model to leverage (useful) domain knowledge together
with general commonsense knowledge for the task. Experimental evaluation on
realworld datasets involving five domains (product types) shows the
effectiveness of the approach.Comment: Accepted to Proceedings of EMNLP 2020 (Findings
Lifelong and Continual Learning Dialogue Systems: Learning during Conversation
Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. 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. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve the situation by endowing the chatbots the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation by themselves so that as they chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and to improve their conversational skills
AI Autonomy: Self-Initiation, Adaptation and Continual Learning
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. As the
real-world is an open environment that is full of unknowns or novelties,
detecting novelties, characterizing them, accommodating or adapting to them,
and gathering ground-truth training data and incrementally learning the
unknowns/novelties are critical to making the AI agent more and more
knowledgeable and powerful over time. The key challenge is how to automate the
process so that it is carried out continually on the agent's own initiative and
through its own interactions with humans, other agents and the environment just
like human on-the-job learning. This paper proposes a framework (called SOLA)
for this learning paradigm to promote the research of building autonomous and
continual learning enabled AI agents. To show feasibility, an implemented agent
is also described.Comment: arXiv admin note: substantial text overlap with arXiv:2110.1138