Few-shot Pseudo-Labeling for Intent Detection

Dopierre, Thomas; Gravier, Christophe; Subercaze, Julien; Logerais, Wilfried

doi:10.18653/v1/2020.coling-main.438

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…As the important part of a dialog system, dialogue language understanding attract a lot of attention in few-shot scenario. Dopierre et al (2020); Vlasov et al (2018); Xia et al (2018) explored fewshot intent detection technique. Luo et al (2018) and Hou et al (2020a) investigated few-shot slot tagging by using prototypical network.…”

Section: Related Workmentioning

confidence: 99%

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

Hou¹,

Lai²,

Cheng³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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In this paper, we investigate few-shot joint learning for dialogue language understanding. Most existing few-shot models learn a single task each time with only a few examples. However, dialogue language understanding contains two closely related tasks, i.e., intent detection and slot filling, and often benefits from jointly learning the two tasks. This calls for new few-shot learning techniques that are able to capture task relations from only a few examples and jointly learn multiple tasks. To achieve this, we propose a similarity-based few-shot learning scheme, named Contrastive Prototype Merging network (ConProm), that learns to bridge metric spaces of intent and slot on data-rich domains, and then adapt the bridged metric space to specific few-shot domain. Experiments on two public datasets, Snips and FewJoint, show that our model significantly outperforms the strong baselines in one and five shots settings.

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Section: Related Workmentioning

confidence: 99%

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

Hou¹,

Lai²,

Cheng³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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“…Pseudo-label selection strategies. We consider several pseudolabel selection strategies, including uncertainty [37], confidence, and clustering [9]. We fix 𝑢 𝑟 to 0.1 on all datasets.…”

Section: Model Variants (Rq4)mentioning

confidence: 99%

PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

Wang¹,

Zeng²,

Chen³

et al. 2022

Preprint

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Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.

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“…They experimentally compare their offered approach to 6 more methods (Matching Network, Prototypical Network, Relation Network, Hybrid Attention-based Prototypical Network, Hierarchical Prototypical Network, Multi-level Matching, and Aggregation Network) and prove their method achieves the best performance on two datasets. A very similar problem [20] is tackled with the novel two-fold pseudolabeling technique. The pseudolabeling process takes embedded user utterances and passes them to a hierarchical clustering method (in a bottom-up tree-manner), then the process goes top-down a tree and expands nodes having multiple labeled sentences with different labels.…”

Section: Related Workmentioning

confidence: 99%

Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

2021

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Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language; (2) there are cross-lingual solutions that work without the training data in the target language. Consequently, in this research, we use the English dataset and solve the intent detection problem for five target languages (German, French, Lithuanian, Latvian, and Portuguese). When seeking the most accurate solutions, we investigate BERT-based word and sentence transformers together with eager learning classifiers (CNN, BERT fine-tuning, FFNN) and lazy learning approach (Cosine similarity as the memory-based method). We offer and evaluate several strategies to overcome the data scarcity problem with machine translation, cross-lingual models, and a combination of the previous two. The experimental investigation revealed the robustness of sentence transformers under various cross-lingual conditions. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered our top-line. However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages.

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Few-shot Pseudo-Labeling for Intent Detection

Cited by 13 publications

References 22 publications

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

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