Authors:
Massimo Ruffolo
1
;
2
and
Francesco Visalli
2
Affiliations:
1
Altilia.ai, Technest - University of Calabria, Piazza Vermicelli, Rende (CS), 87036, Italy, Italy
;
2
High Performance Computing and Networking Institute of the National Research Council (ICAR-CNR), Via Pietro Bucci 8/9C, Rende (CS), 87036, Italy
Keyword(s):
Weak-supervision, Data Programming, Deep Learning, Aspect Based Sentiment Analysis, Transformers, Natural Language Processing.
Abstract:
Aspect Based Sentiment Analysis (ABSA) is receiving growing attention from the research community because it has applications in several real world use cases. To train deep learning models for ABSA in vertical domains may result a laborious process requiring a significative human effort in creating proper training sets. In this work we present initial studies regarding the definition of an easy-to-use, flexible, and reusable weakly-supervised method for the Aspect Sentence Classification task of ABSA. Our method mainly consists in a process where templates of Labeling Functions automatically annotate sentences, and then the generative model of Snorkel constructs a probabilistic training set. In order to test effectiveness and applicability of our method we trained machine learning models where the loss function is informed about the probabilistic nature of the labels. In particular, we fine-tuned BERT models on two famous disjoint SemEval datasets related to laptops and restaurants.