PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

Mukherjee, Rajdeep; Nayak, Tapas; Butala, Yash; Bhattacharya, Sourangshu; Goyal, Pawan

doi:10.18653/v1/2021.emnlp-main.731

Cited by 28 publications

(27 citation statements)

References 25 publications

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“…TwoStage [12], JET [91], GTS [75], OTE-MTL [92], BMRC [93] Dual-MRC [94], GAS [95], Gen-ABSA [96], Span-ASTE [97], NAG-ASTE [98], PASTE [99] Aspect-Category-Sentiment Detection (ACSD) TAS-BERT [46], MEJD [100], GAS [95], Paraphrase [7] Quad Extraction Aspect Sentiment Quad Prediction (ASQP) Extract-Classify-ACOS [101], Paraphrase [7] Fig. 2.…”

Section: Aspect Sentiment Triplet Extraction (Aste)mentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks have been introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

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Section: Aspect Sentiment Triplet Extraction (Aste)mentioning

confidence: 99%

“…6(c)) [93,94] and Seq2Seq modeling (see Fig. 6(d)) [96,95,99,98] The pizza is delicious but the service is terrible…”

Section: Aspect Sentiment Triplet Extraction (Aste)mentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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“…With the exponential growth of online marketplaces and user-generated content therein, SSA or near similar tasks of aspect-sentiment-opinion triplet extraction (Peng et al, 2020;Mukherjee et al, 2021a;Yan et al, 2021), and aspect-categorysentiment-opinion quad extraction (Cai et al, 2021), the newest additions under the broader umbrella of aspect-based sentiment analysis (ABSA) (Pontiki et al, 2014a,b) have become more important than ever (Mukherjee et al, 2021b). In the face of ever-expanding choices, it becomes a challenging necessity to take educated explainable decisions from past user reviews.…”

Section: Equal Contributionmentioning

confidence: 99%

ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach

Raghav¹,

Vemali²,

Mukherjee³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval-2022 shared task. We leverage a BART-based encoderdecoder architecture and suitably modify it to generate, given a sentence, a sequence of opinion tuples. Each generated tuple consists of seven integers respectively representing the indices corresponding to the start and end positions of the holder, target, and expression spans, followed by the sentiment polarity class associated between the target and the sentiment expression. We perform rigorous experiments for both Monolingual and Cross-lingual subtasks, and achieve competitive Sentiment F1 scores on the leaderboard in both settings.

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“…Chen et al [51] proposed two transformer-based decoders for the ATE, OTE, and ALSC tasks. Mukherjee et al [52] developed a pointer network-based model for the aspect sentiment triplet extraction. Besides, sufficient annotated data is essential in ATE.…”

Section: Related Workmentioning

confidence: 99%

Aspect term extraction via information-augmented neural network

Liu

Shen

2022

Complex Intell. Syst.

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Aspect term extraction (ATE) aims at identifying the aspect terms that are expressed in a sentence. Recently, Seq2Seq learning has been employed in ATE and significantly improved performance. However, it suffers from some weaknesses, such as lacking the ability to encode the more informative information and integrate information of surrounding words in the encoder. The static word embeddings employed in ATE fall short of modeling the dynamic meaning of words. To alleviate the problems mentioned above, this paper proposes the information-augmented neural network (IANN) which is a novel Seq2Seq learning framework. In IANN, a specialized neural network is developed as the key module of the encoder, named multiple convolution with recurrence network (MCRN), to encode the more informative information and integrate information of surrounding words in the encoder. The contextualized embedding layer is designed to capture the dynamic word sense. Besides, the novel AO ({Aspect, Outside}) tags are proposed as the less challenging tagging scheme. A lot of experiments have been performed on three widely used datasets. These experiments demonstrate that the proposed IANN acquires state-of-the-art results and validate that the proposed IANN is a powerful method for the ATE task.

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PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

Cited by 28 publications

References 25 publications

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach

Aspect term extraction via information-augmented neural network

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