Papers with slot filling task
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (2021.naacl-main)
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| Challenge: | Recent research shows promising results by jointly learning of slot filling and intent detection tasks. |
| Approach: | They propose a way to combine slot filling and slot filler learning to achieve state-of-the-art results. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets and ATIS datasets. |
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (N18-2)
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| Challenge: | Existing systems that can understand natural language questions and generate corresponding SQL queries are not able to do this. |
| Approach: | They propose a novel approach which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions. |
| Outcome: | The proposed approach outperforms the prior art on the WikiSQL dataset and can reach 82.6% accuracy, a 17.5% improvement compared to the previous content-sensitive model. |
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (2020.emnlp-main)
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| Challenge: | Slot filling and intent detection are two main tasks in spoken language understanding systems. |
| Approach: | They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem. |
| Outcome: | The proposed model significantly outperforms previous models in slot filling task while speeding up decoding. |
Federated Learning for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing methods to improve robustness of models focus on a single dataset . but, there are few studies on how to combine merits of different datasets . |
| Approach: | They propose a federated learning framework that could unify datasets and tasks . they propose MV-Encoder as backbone of the framework to provide multi-granularity text representations . |
| Outcome: | The proposed framework improves on two SLU benchmark datasets and federated learning settings. |
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding (2022.lrec-1)
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Salima Mdhaffar, Valentin Pelloin, Antoine Caubrière, Gaëlle Laperriere, Sahar Ghannay, Bassam Jabaian, Nathalie Camelin, Yannick Estève
| Challenge: | Pretrained models have been introduced for both acoustic and language modeling. |
| Approach: | They present an error analysis of pretrained models using a french MEDIA benchmark dataset. |
| Outcome: | The proposed models have been able to improve on the french MEDIA benchmark dataset, which is one of the most challenging among all benchmarks accessible to the entire research community. |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
Neuralizing Regular Expressions for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods to integrate neural networks and symbolic rules have their merits and weaknesses. |
| Approach: | They propose to integrate regular expressions into neural networks for a slot filling task . they use finite-state transducers to convert regular expression into a neural network . their model has superior zero-shot and few-shot performance . |
| Outcome: | The proposed model outperforms rules in zero-shot and few-shot scenarios and is competitive when training data is available. |