Papers by Aleksandr Livshits
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing (2023.eacl-main)
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Akshat Shrivastava, Shrey Desai, Anchit Gupta, Ali Elkahky, Aleksandr Livshits, Alexander Zotov, Ahmed Aly
| Challenge: | Task-oriented semantic parsing models have achieved strong results in recent years, but they often face obstacles adapting to novel settings with distinct semantics and scarce data. |
| Approach: | They propose a scenario-based semantic parsing model which isolates coarse-grained and fine-grounded aspects of the task and solves them with off-the-shelf neural modules. |
| Outcome: | The proposed model outperforms previous approaches in high-resource, low-resourced, and multilingual settings, and is modular, differentiable, interpretable, and allows extra supervision from scenarios. |
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding (2024.findings-emnlp)
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Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
| Challenge: | End-to-end models for Spoken Language Understanding have been autoregressive, resulting in higher latencies. |
| Approach: | They propose a method that uses Connectionist Temporal Classification to train robust non-autoregressive deliberation models. |
| Outcome: | The proposed method achieves 10x latency reduction over autoregressive models while preserving ability to correct ASR mistranscriptions. |
Treepiece: Faster Semantic Parsing via Tree Tokenization (2023.findings-emnlp)
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| Challenge: | Autoregressive (AR) encoder-decoder neural networks are slow in sequential prediction of natural language to machine-readable parse trees. |
| Approach: | They propose a technique that tokenizes a parse tree into subtrees and generates one subtrea per decoding step. |
| Outcome: | The proposed approach shows 4.6 times faster decoding speed and comparable speed but significantly higher accuracy compared to non-autoregressive (NAR) models. |