Papers with TOPv2
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing (2021.findings-emnlp)
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Akshat Shrivastava, Pierce Chuang, Arun Babu, Shrey Desai, Abhinav Arora, Alexander Zotov, Ahmed Aly
| Challenge: | a novel approach to map utterances to semantic frames is based on non-autoregressive parsers that shift the decoding task from text generation to span prediction. |
| Approach: | They propose a non-autoregressive, task-oriented parser which shifts the decoding task from text generation to span prediction and produces endpoints as opposed to text. |
| Outcome: | The proposed model bridges the quality gap between non-autoregressive and autoregressive parsers, achieving 87 EM on TOPv2 and shows a 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-regressives. |
Label Semantic Aware Pre-training for Few-shot Text Classification (2022.acl-long)
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| Challenge: | Existing models for text classification use label semantics but few studies have attempted to give models access to informative representations of labels. |
| Approach: | They propose to use label semantics to train generative models by performing secondary pre-training on labeled sentences from a variety of domains. |
| Outcome: | The proposed approach improves generalization and data efficiency of text classification systems while maintaining comparable performance to state-of-the-art models. |