791 research outputs found
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
Reinforcement learning (RL) has emerged as a powerful approach for tackling
complex medical decision-making problems such as treatment planning,
personalized medicine, and optimizing the scheduling of surgeries and
appointments. It has gained significant attention in the field of Natural
Language Processing (NLP) due to its ability to learn optimal strategies for
tasks such as dialogue systems, machine translation, and question-answering.
This paper presents a review of the RL techniques in NLP, highlighting key
advancements, challenges, and applications in healthcare. The review begins by
visualizing a roadmap of machine learning and its applications in healthcare.
And then it explores the integration of RL with NLP tasks. We examined dialogue
systems where RL enables the learning of conversational strategies, RL-based
machine translation models, question-answering systems, text summarization, and
information extraction. Additionally, ethical considerations and biases in
RL-NLP systems are addressed
Sources of Noise in Dialogue and How to Deal with Them
Training dialogue systems often entails dealing with noisy training examples
and unexpected user inputs. Despite their prevalence, there currently lacks an
accurate survey of dialogue noise, nor is there a clear sense of the impact of
each noise type on task performance. This paper addresses this gap by first
constructing a taxonomy of noise encountered by dialogue systems. In addition,
we run a series of experiments to show how different models behave when
subjected to varying levels of noise and types of noise. Our results reveal
that models are quite robust to label errors commonly tackled by existing
denoising algorithms, but that performance suffers from dialogue-specific
noise. Driven by these observations, we design a data cleaning algorithm
specialized for conversational settings and apply it as a proof-of-concept for
targeted dialogue denoising.Comment: 23 pages, 6 Figures, 5 tables. Accepted at SIGDIAL 202
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