Papers by Michael Kayser
The Alexa Meaning Representation Language (N18-3)
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Thomas Kollar, Danielle Berry, Lauren Stuart, Karolina Owczarzak, Tagyoung Chung, Lambert Mathias, Michael Kayser, Bradford Snow, Spyros Matsoukas
| Challenge: | a new meaning representation language for spoken language is introduced for Alexa . AMRL provides a common representation for how people communicate in spoken language . there is no mechanism to represent ambiguity, forcing the choice of a fixed interpretation for ambiguous utterances. |
| Approach: | They introduce a meaning representation for spoken language, the Alexa meaning represent language . they use a spoken language dataset to collect a sample of utterances from eight domains . |
| Outcome: | The proposed representation provides a common representation for spoken language understanding . it supports cross-domain queries, fine-grained types, complex utterances and composition . the proposed representation was released to developers at a trade show in 2016 . |
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings (2020.findings-emnlp)
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Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao, Subendhu Rongali, Haidar Khan, Michael Kayser
| Challenge: | Existing task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders. |
| Approach: | They propose to learn compositional code embeddings to greatly reduce the sizes of BERT and RoBERTa encoders. |
| Outcome: | The proposed model reduces the size of BERT and RoBERTa encoders while maintaining performance. |