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When Mostafazadeh et al. However, their best performing model the deep structured semantic model could only achieve a 7.
Mostafazadeh et al. In other words, the model can predict the story ending without having read the story!
- The Washington Post
Cai et al. In EncWords, a bi-LSTM RNN is trained to encode each word of a sentence, and the sentence representation is obtained by adding the forward and backward vectors at each word and then averaging across words. EncWords and EncSents are used to build two models of multi-sentence representation. After words, sentences, and multi-sentence sequences can be encoded, Cai et al. Furthermore, they test an attention mechanism based on the representation of a candidate ending.
A feed-forward network takes the final representation of the story and outputs a score for the likelihood of the story.
History’s Biggest Common Sense Moments
Hinge loss is minimized between the score for the correct and incorrect ending. Without attention, the Story scoring outperforms the PlotEnd story across both Hier and Flat encodings. Furthermore, some common words appear exclusively in correct or exclusively in incorrect endings. These results highlight that appropriate baselines are needed to account for biases in NLP tasks: constant-choose-first accuracy is an overly generous baseline!
Mr. Common Sense has Passed Away
Sign in. It could be about body image, how men and women relate to one another, it could be about different ethnicities. I think for all kids we need to be very concerned with what those messages are and be very aware there are media entities communicating these messages to kids.
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