recurrent neural networks called teacher forcingas illustrated in figure - the key idea here is to use ( instead of yt - in the computation of (twhile training while making predictions (when the model is deployed for usagehoweveryt - is used idirectional rnns let' now look at another variation on rnnsthe bidirectional rnn the key idea behind bidirectional rnn is to use the entities that lie further in the sequence to make prediction for the current entity for all the rnns we have considered so farwe have been using the previous entities (captured by the hidden stateand the current entity in the sequence to make the prediction howeverwe have not been using information concerning the entities that lie further in the sequence to make predictions bidirectional rnn leverages this information and can give improved predictive accuracy in many cases (figure - consider the following simple exampleborrowed from andrew ng' coursera lecture sentence he said"teddy bears are beautiful toys sentence he said"teddy rooseveltthe president of united states in these sentencesconsidering classic case of nlp (predicting the next word)there is no means to correctly predict the word after "teddy(assuming unidirectional forward rnnthe context that comes from the right side essentially sheds light on an accurate prediction for the next word consider sentiment analysis task where model is trying to classify sentences as positive or negative with the left and right context building in the networkbidirectional models can effectively "look forwardin the sentence to see if "futuretokens may influence the current decision in