Related papers: Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification

Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification

URL: http://arxiv.org/abs/2010.08318v2
Date: Wed, 31 Mar 2021 08:17:40 GMT
Title: Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
Authors: Andrew Moore and Jeremy Barnes
Abstract summary: We show that targeted sentiment models are not robust to linguistic phenomena, specifically negation and speculation. We propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection. We create two challenge datasets to evaluate model performance on negated and speculative samples.
Score: 15.85111852764517
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation. In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena. Further we create two challenge datasets to evaluate model performance on negated and speculative samples. We find that multi-task models and transfer learning via language modelling can improve performance on these challenge datasets, but the overall performances indicate that there is still much room for improvement. We release both the datasets and the source code at https://github.com/jerbarnes/multitask_negation_for_targeted_sentiment.

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