119 research outputs found
Context-aware Helpfulness Prediction for Online Product Reviews
Modeling and prediction of review helpfulness has become more predominant due
to proliferation of e-commerce websites and online shops. Since the
functionality of a product cannot be tested before buying, people often rely on
different kinds of user reviews to decide whether or not to buy a product.
However, quality reviews might be buried deep in the heap of a large amount of
reviews. Therefore, recommending reviews to customers based on the review
quality is of the essence. Since there is no direct indication of review
quality, most reviews use the information that ''X out of Y'' users found the
review helpful for obtaining the review quality. However, this approach
undermines helpfulness prediction because not all reviews have statistically
abundant votes. In this paper, we propose a neural deep learning model that
predicts the helpfulness score of a review. This model is based on
convolutional neural network (CNN) and a context-aware encoding mechanism which
can directly capture relationships between words irrespective of their distance
in a long sequence. We validated our model on human annotated dataset and the
result shows that our model significantly outperforms existing models for
helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517522Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias). Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur, color temperature, day/night). We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions. In simulation studies, the approach not only enhanced prediction accuracy, but also generated highly faithful explanations about these predictions as if the images were unbiased. In user studies, debiased explanations improved user task performance, perceived truthfulness and perceived helpfulness. Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.Peer ReviewedPostprint (published version
Goal-Driven Sequential Data Abstraction
Automatic data abstraction is an important capability for both benchmarking
machine intelligence and supporting summarization applications. In the former
one asks whether a machine can `understand' enough about the meaning of input
data to produce a meaningful but more compact abstraction. In the latter this
capability is exploited for saving space or human time by summarizing the
essence of input data. In this paper we study a general reinforcement learning
based framework for learning to abstract sequential data in a goal-driven way.
The ability to define different abstraction goals uniquely allows different
aspects of the input data to be preserved according to the ultimate purpose of
the abstraction. Our reinforcement learning objective does not require
human-defined examples of ideal abstraction. Importantly our model processes
the input sequence holistically without being constrained by the original input
order. Our framework is also domain agnostic -- we demonstrate applications to
sketch, video and text data and achieve promising results in all domains.Comment: Accepted at ICCV 201
An assessment of deep learning models and word embeddings for toxicity detection within online textual comments
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task
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