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- 저자 : Salahuddin Muhammad Iqbal (검색결과 1 건)
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Subword Neural Language Generation with Unlikelihood Training
Salahuddin Muhammad Iqbal,Dae-Ki Kang 한국인터넷방송통신학회 2020 International Journal of Internet, Broadcasting an Vol.12 No.2
A Language model with neural networks commonly trained with likelihood loss. Such that the model can learn the sequence of human text. State-of-the-art results achieved in various language generation tasks, e.g., text summarization, dialogue response generation, and text generation, by utilizing the language model’s next token output probabilities. Monotonous and boring outputs are a well-known problem of this model, yet only a few solutions proposed to address this problem. Several decoding techniques proposed to suppress repetitive tokens. Unlikelihood training approached this problem by penalizing candidate tokens probabilities if the tokens already seen in previous steps. While the method successfully showed a less repetitive generated token, the method has a large memory consumption because of the training need a big vocabulary size. We effectively reduced memory footprint by encoding words as sequences of subword units. Finally, we report competitive results with token level unlikelihood training in several automatic evaluations compared to the previous work.
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