论文阅读:Diverse Beam Search--Decoding Diverse Solutions from Neural Sequence Models,程序员大本营,技术文章内容聚合第一站。
Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS)... Q Sun,S Lee,D Bat...
The decoding (translation) process is started as soon as the decoder receives a starting symbol "" (refer astgt_sos_idin our code); For each timestep on the decoder side, we treat the RNN's output as a set of logits. We choose the most likely word, the id associated with the maxim...
However, the frequently used random methods such as sampling or noised beam search, although can output diverse back-translations, often generate noisy synthetic sentences. To alleviate this problem, we propose a simple but effective constraint random decoding method for back-translation. The proposed ...
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models Neural sequence models are widely used to model time-series data in many fields. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS ...
However, beam search always focuses on the head of the model distribution, which results in very regular translation hypotheses that do not adequately cover the actual data distribution [30]. On the contrary, decoding based on sampling or restricted sampling can produce diverse data by sampling fro...