Zero-shot User Intent Detection via Capsule Neural Networks

Xia, Congying; Zhang, Chenwei; Yan, Xiaohui; Chang, Yi; Yu, Philip S.

doi:10.18653/v1/d18-1348

Cited by 176 publications

(138 citation statements)

References 19 publications

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“…But they report R@K, so it is unclear whether the system can really predict unseen labels. Xia et al (2018) study the zero-shot intent detection problem. The learned representations of intents are still the sum of word embeddings.…”

Section: Related Workmentioning

confidence: 99%

“…Prior work is mostly evaluated on different datasets and adopted different evaluation setups, which makes it hard to compare them fairly. For example, Rios and Kavuluru (2018) work on medical data while reporting R@K as metric; Xia et al (2018) work on SNIPS-NLU intent detection data while only unseen intents are in the label-searching space in evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Yin

Hay

Roth

2019

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

316

303

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Zero-shot text classification (0SHOT-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0SHOT-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label. And there are only a few articles studying 0SHOT-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0SHOT-TC. In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress.This work benchmarks the 0SHOT-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines. Our contributions include: i) The datasets we provide facilitate studying 0SHOT-TC relative to conceptually different and diverse aspects: the "topic" aspect includes "sports" and "politics" as labels; the "emotion" aspect includes "joy" and "anger"; the "situation" aspect includes "medical assistance" and "water shortage". ii) We extend the existing evaluation setup (labelpartially-unseen) -given a dataset, train on some labels, test on all labels -to include a more challenging yet realistic evaluation label-fully-unseen 0SHOT-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0SHOT-TC of diverse aspects within a textual entailment formulation and study it this way. 1The plague in Mongolia, occurring last week, has caused more than a thousand isolation health,

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Yin

Hay

Roth

2019

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

316

303

View full text Add to dashboard Cite

show abstract

“…Unlike their work, we study few-shot text classification. Xia et al (2018) reused the supervised model similar to that of for intent classification, in which a capsule-based architecture is extended to compute similarity between the target intents and source intents. Unlike their work, we propose Induction Networks for few-shot learning, in which we propose to use capsules and dynamic routing to learn generalized class-level representation from samples based.…”

Section: Capsule Networkmentioning

confidence: 99%

Induction Networks for Few-Shot Text Classification

Geng¹,

Li²,

Li³

et al. 2019

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

170

122

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Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the samplewise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification. * * Corresponding authors: Y.Li and P.Jian.

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“…However, diverse user expressions may make it difficult to learn a good projection function and thus affect the classification performance. Recently, Xia et al (2018) extend capsule networks for zero-shot intent classification by transferring the prediction vectors from seen classes to unseen classes. However, there are some key issues left to be resolved, including how to deal with polysemy in word embeddings and how to improve the model generalization ability to unseen intents in the generalized zero-shot intent classification setting.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing Capsule Networks for Zero-shot Intent Classification

Liu¹,

Zhang²,

Lü³

et al. 2019

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

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Intent classification is an important building block of dialogue systems. With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zeroshot intent classification. Nevertheless, research on this problem is still in the incipient stage and few methods are available. A recently proposed zero-shot intent classification method, IntentCapsNet, has been shown to achieve state-of-the-art performance. However, it has two unaddressed limitations: (1) it cannot deal with polysemy when extracting semantic capsules; (2) it hardly recognizes the utterances of unseen intents in the generalized zero-shot intent classification setting. To overcome these limitations, we propose to reconstruct capsule networks for zeroshot intent classification. First, we introduce a dimensional attention mechanism to fight against polysemy. Second, we reconstruct the transformation matrices for unseen intents by utilizing abundant latent information of the labeled utterances, which significantly improves the model generalization ability. Experimental results on two task-oriented dialogue datasets in different languages show that our proposed method outperforms IntentCapsNet and other strong baselines.

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Zero-shot User Intent Detection via Capsule Neural Networks

Cited by 176 publications

References 19 publications

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Induction Networks for Few-Shot Text Classification

Reconstructing Capsule Networks for Zero-shot Intent Classification

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