Aspect Based Sentiment Analysis into the Wild

Brun, Catherine; Nikoulina, Vassilina

doi:10.18653/v1/w18-6217

Cited by 23 publications

(11 citation statements)

References 13 publications

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“…Building Natural Language Processing models that perform well in the wild is still an open and challenging problem. It is well known that modern machine-learning models can be brittle, meaning that -even when achieving impressive performance on the evaluation set -their performance can degrade significantly when exposed to new examples with differences in vocabulary and writing style Jia and Liang, 2017;Brun and Nikoulina, 2018). This drop in performance when changing from domain D s to domain D t can be due to a variety of causes.…”

Section: Introductionmentioning

confidence: 99%

To Annotate or Not? Predicting Performance Drop under Domain Shift

Elsahar¹,

Gallé²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Performance drop due to domain-shift is an endemic problem for NLP models in production. This problem creates an urge to continuously annotate evaluation datasets to measure the expected drop in the model performance which can be prohibitively expensive and slow. In this paper we study the problem of predicting the performance drop of modern NLP models under domain-shift, in the absence of any target domain labels. We investigate three families of methods (H-divergence, reverse classification accuracy and confidence measures), show how they can be used to predict the performance drop and study their robustness to adversarial domain-shifts. Our results on sentiment classification and sequence labeling show that our method is able to predict performance drops with an error rate as low as 2.15% and 0.89% for sentiment analysis and POS tagging respectively.

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Section: Introductionmentioning

confidence: 99%

To Annotate or Not? Predicting Performance Drop under Domain Shift

Elsahar¹,

Gallé²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…We used the published code to evaluate on both Res15 and Res16. • baseline-1-f lex: baseline-1-f lex (Brun and Nikoulina 2018) is a pipeline method for TASD. The source code is not provided and the paper only reported results on Res15 for both TASD and its subtask ASD.…”

Section: Comparison Methodsmentioning

confidence: 99%

“…Only the TASD task can capture this dual dependence, which aims to jointly detect target-aspect-sentiment triples. As far as we know, there is only one study (Brun and Nikoulina 2018) addressing the TASD task. It proposes a method relying on available parsers and domain-specific semantic lexicons, but this method performs poorly as shown in our experiments.…”

Section: Related Workmentioning

confidence: 99%

Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Wan¹,

Yang²,

et al. 2020

AAAI

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Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.

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“…Baseline (Brun and Nikoulina, 2018) -38.10 TAS-LPM-CRF (Wan et al, 2020) 54.76 64.66 TAS-SW-CRF (Wan et al, 2020) 57.51 65.89 TAS-SW-TO (Wan et al, 2020) 58.09 65.44 all experiments. T5 closely follows the original encoder-decoder architecture of the Transformer model, with some slight differences such as different position embedding schemes.…”

Section: Rest15 Rest16mentioning

confidence: 99%

Towards Generative Aspect-Based Sentiment Analysis

Zhang¹,

Li²,

Deng³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional taskspecific model design. 1

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Aspect Based Sentiment Analysis into the Wild

Cited by 23 publications

References 13 publications

To Annotate or Not? Predicting Performance Drop under Domain Shift

To Annotate or Not? Predicting Performance Drop under Domain Shift

Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Towards Generative Aspect-Based Sentiment Analysis

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