36 research outputs found
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user
engagement. Automatically making comments thus become a valuable functionality
for online forums, intelligent chatbots, etc. This paper proposes the new task
of automatic article commenting, and introduces a large-scale Chinese dataset
with millions of real comments and a human-annotated subset characterizing the
comments' varying quality. Incorporating the human bias of comment quality, we
further develop automatic metrics that generalize a broad set of popular
reference-based metrics and exhibit greatly improved correlations with human
evaluations.Comment: ACL2018; with supplements; Dataset link available in the pape
From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Passively collected behavioral health data from ubiquitous sensors holds
significant promise to provide mental health professionals insights from
patient's daily lives; however, developing analysis tools to use this data in
clinical practice requires addressing challenges of generalization across
devices and weak or ambiguous correlations between the measured signals and an
individual's mental health. To address these challenges, we take a novel
approach that leverages large language models (LLMs) to synthesize clinically
useful insights from multi-sensor data. We develop chain of thought prompting
methods that use LLMs to generate reasoning about how trends in data such as
step count and sleep relate to conditions like depression and anxiety. We first
demonstrate binary depression classification with LLMs achieving accuracies of
61.1% which exceed the state of the art. While it is not robust for clinical
use, this leads us to our key finding: even more impactful and valued than
classification is a new human-AI collaboration approach in which clinician
experts interactively query these tools and combine their domain expertise and
context about the patient with AI generated reasoning to support clinical
decision-making. We find models like GPT-4 correctly reference numerical data
75% of the time, and clinician participants express strong interest in using
this approach to interpret self-tracking data