Papers with unconditioned language model
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue (D19-1)
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| Challenge: | Existing methods to generate natural language for task-oriented dialogues lack naturalness and variation in language. |
| Approach: | They propose a multi-task learning framework for natural language generation that explicitly targets for naturalness in generated responses via an unconditioned language model. |
| Outcome: | The proposed framework outperforms existing models across multiple datasets in the study of natural language generation. |
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)
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| Challenge: | Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST). |
| Approach: | They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property. |
| Outcome: | The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model. |