20 research outputs found
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
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
IndoNLI : a Natural Language Inference Dataset for Indonesian
We present IndoNLI, the first human-elicited NLI dataset for Indonesian. We adapt the data collection protocol for MNLI and collect ~18K sentence pairs annotated by crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Experiment results show that XLM-R outperforms other pre-trained models in our data. The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging. Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data. We hope this dataset can help accelerate progress in Indonesian NLP research
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