| Autor: |
Tarasiewicz, Piotr, Kenjeyev, Sultan, Sebag, Ilana, Alshehabi, Shehab |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
NeurIPS Workshop on Efficient Natural Language and Speech Processing, 2021 |
| Druh dokumentu: |
Working Paper |
| Popis: |
The recent emergence of deep learning methods has enabled the research community to achieve state-of-the art results in several domains including natural language processing. However, the current robocall system remains unstable and inaccurate: text generator and chat-bots can be tedious and misunderstand human-like dialogue. In this work, we study the performance of two models able to enhance an intelligent conversational agent through adversarial conversational shaping: a generative adversarial network with policy gradient (GANPG) and a generative adversarial network with reward for every generation step (REGS) based on the REGS model presented in Li et al. [18] . This model is able to assign rewards to both partially and fully generated text sequences. We discuss performance with different training details : seq2seq [ 36] and transformers [37 ] in a reinforcement learning framework. |
| Databáze: |
arXiv |
| Externí odkaz: |
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