10 research outputs found
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201
Few Shot Rationale Generation using Self-Training with Dual Teachers
Self-rationalizing models that also generate a free-text explanation for
their predicted labels are an important tool to build trustworthy AI
applications. Since generating explanations for annotated labels is a laborious
and costly pro cess, recent models rely on large pretrained language models
(PLMs) as their backbone and few-shot learning. In this work we explore a
self-training approach leveraging both labeled and unlabeled data to further
improve few-shot models, under the assumption that neither human written
rationales nor annotated task labels are available at scale. We introduce a
novel dual-teacher learning framework, which learns two specialized teacher
models for task prediction and rationalization using self-training and distills
their knowledge into a multi-tasking student model that can jointly generate
the task label and rationale. Furthermore, we formulate a new loss function,
Masked Label Regularization (MLR) which promotes explanations to be strongly
conditioned on predicted labels. Evaluation on three public datasets
demonstrate that the proposed methods are effective in modeling task labels and
generating faithful rationales.Comment: ACL Findings 202
METHODS FOR IMPROVING USABILITY OF ONLINE USER GENERATED CONTENT
Ph.DDOCTOR OF PHILOSOPHY (SOC
Cold Start Thread Recommendation as Extreme Multi-label Classification
10.1145/3184558.3191659XMLC for Social Media: WWW 2018 Companion: The 2018 Web Conference Companion1911-191