Augmented Natural Language for Generative Sequence Labeling

Athiwaratkun, Ben; Santos, Cícero Nogueira dos; Krone, Jason; Xiang, Bing

doi:10.18653/v1/2020.emnlp-main.27

Cited by 36 publications

(29 citation statements)

References 25 publications

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“…The main idea is to simulate fast update scenarios during training, and update the model's parameters so that the model performs fast updates more efficiently. Fast update with meta learning has been applied to NLP models for generalizing to unseen tasks or domains (Gu et al, 2018a;Dou et al, 2019;Bansal et al, 2020;Athiwaratkun et al, 2020;Wang et al, 2021).…”

Section: Metric Learningmentioning

confidence: 99%

Controllable Semantic Parsing via Retrieval Augmentation

Pasupat¹,

Zhang²,

Guu³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving stateof-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.

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Section: Metric Learningmentioning

confidence: 99%

Controllable Semantic Parsing via Retrieval Augmentation

Pasupat¹,

Zhang²,

Guu³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Inspired by recent success in formulating various NLP tasks as text generation problems (Athiwaratkun et al, 2020;Paolini et al, 2021;, we propose to tackle ASQP in a sequenceto-sequence (S2S) manner in this paper. On one hand, the sentiment quads can be predicted in an end-to-end manner, alleviating the potential error propagation in the pipeline solutions.…”

Section: Introductionmentioning

confidence: 99%

Aspect Sentiment Quad Prediction as Paraphrase Generation

Zhang¹,

Deng²,

Li³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel PARAPHRASE modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of crosstask transfer with the proposed unified PARA-PHRASE modeling framework.

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“…Motivated by recent success in formulating sev-eral language understanding problems such as named entity recognition, question answering, and text classification as generation tasks (Raffel et al, 2020;Athiwaratkun et al, 2020), we propose to tackle various ABSA problems in a unified generative approach in this paper. It can fully utilize the rich label semantics by encoding the natural language label into the target output.…”

Section: Introductionmentioning

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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Augmented Natural Language for Generative Sequence Labeling

Cited by 36 publications

References 25 publications

Controllable Semantic Parsing via Retrieval Augmentation

Controllable Semantic Parsing via Retrieval Augmentation

Aspect Sentiment Quad Prediction as Paraphrase Generation

Towards Generative Aspect-Based Sentiment Analysis

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