Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems

Panda, Subhadarshi; Tırkaz, Çağlar; Falke, Tobias; Lehnen, Patrick

doi:10.18653/v1/2021.nlp4convai-1.4

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Paraphrasing the data is one of the ways frequently used for augmentation and can produce more diverse synthetic text with different word choices and sentence structures while preserving the meaning of the original text. Paraphrasing methods have been shown to be effective in many natural language processing tasks (Gupta et al, 2018;Edunov et al, 2018;Iyyer et al, 2018;Wei and Zou, 2019;Cai et al, 2020;Okur et al, 2022;Panda et al, 2021;Jolly et al, 2020). However, such methods often fail to generate more challenging and semantically diverse sentences that are important for the robustness of the downstream models.…”

Section: Related Workmentioning

confidence: 99%

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Lin,

Papangelis,

Kim

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pretrained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise Vinformation (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints -utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).

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Section: Related Workmentioning

confidence: 99%

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Lin,

Papangelis,

Kim

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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“…Recent studies explore data augmentation via Natural Language Generation (NLG) for few-shot intents (Xia et al, 2020) and paraphrase generation for intents and slots in task-oriented dialogue systems (Jolly et al, 2020). Another relevant recent work (Panda et al, 2021) is an extension of a transformer-based model by Jolly et al (2020) that works for multilingual paraphrase generation for intents and slots, even in the zeroshot settings. Several other recent works have also been exploring data augmentation with fine-tuning large language models and few-shot learning for intent classification and slot-filling tasks (Kumar et al, 2019;Kumar et al, 2020;Lee et al, 2021).…”

Section: Data Augmentationmentioning

confidence: 99%

Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System

Okur¹,

Sahay²,

Nachman³

2022

Preprint

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Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.

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“…Paraphrase generation (PG) (Madnani and Dorr, 2010) is to rephrase a sentence into an alternative expression with the same semantics, which has been applied to many downstream tasks, such as question answering (Gan and Ng, 2019) and dialogue systems (Jolly et al, 2020;Gao et al, 2020;Panda et al, 2021;Liang et al, 2019Liang et al, , 2021Liang et al, , 2022. On this basis, to improve the syntactic diversity of paraphrases, syntactically-controlled paraphrase Figure 1: The generated paraphrases with different templates.…”

Section: Introductionmentioning

confidence: 99%

A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation

Zhang,

Liang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Existing syntactically-controlled paraphrase generation (SPG) models perform promisingly with human-annotated or well-chosen syntactic templates. However, the difficulty of obtaining such templates actually hinders the practical application of SPG models. For one thing, the prohibitive cost makes it unfeasible to manually design decent templates for every source sentence. For another, the templates automatically retrieved by current heuristic methods are usually unreliable for SPG models to generate qualified paraphrases. To escape this dilemma, we propose a novel Quality-based Syntactic Template Retriever (QSTR) to retrieve templates based on the quality of the to-be-generated paraphrases. Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality. Experiments demonstrate that QSTR can significantly surpass existing retrieval methods in generating high-quality paraphrases and even perform comparably with human-annotated templates in terms of reference-free metrics. Additionally, human evaluation and the performance on downstream tasks using our generated paraphrases for data augmentation showcase the potential of our QSTR and DTS algorithm in practical scenarios.

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Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems

Cited by 4 publications

References 20 publications

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System

A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation

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