Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.Comment: Accepted at PACLIC 2020 - The 34th Pacific Asia Conference on Language, Information and Computatio

leveraging non conversational tasks for low resource slot filling does it help

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exploring named entity recognition as an auxiliary task for slot filling in conversational language understanding

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From General to Specific: Leveraging Named Entity Recognition for Slot Filling in Conversational Language Understanding

Slot filling techniques are often adopted in language understanding components for task-oriented dialogue systems. In recent approaches, neural models for slot filling are trained on domain-specific datasets, making it difficult porting to similar domains when few or no training data are available. In this paper we use multi-task learning to leverage general knowledge of a task, namely Named Entity Recognition (NER), to improve slot filling performance on a semantically similar domain-specific task. Our experiments show that, for some datasets, transfer learning from NER can achieve competitive performance compared with the state-of-the-art and can also help slot filling in low resource scenarios.Molti sistemi di dialogo taskoriented utilizzano tecniche di slot-filling per la comprensione degli enunciati. Gli approcci piú recenti si basano su modelli neurali addestrati su dataset specializzati per un certo dominio, rendendo difficile la portabilitá su dominii simili, quando pochi o nessun dato di addestramento é disponibile. In questo contributo usiamo multitask learning per sfruttare la conoscenza generale proveniente da un task, precisamente Named Entity Recognition (NER), per migliorare le prestazioni di slot filling su dominii specifici e semanticamente simili. I nostri esperimenti mostrano che transfer learning da NER aiuta lo slot filling in dominii con poche risorse e raggiunge risultati competitivi con lo stato dell’arte

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Table of Contents

Acknowledgements i

Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models