Hangman is a popular word game. Person 1 chooses a word at his will and the goal of Person 2 is to predict the word by predicting one character at a time. At every step, Person 1 gives the following feedback to Person 2 depending on the predicted character:

1. If the character is present, Person 1 informs Person 2 of all the positions at which the character is present. e.g. if the word is 'hello' and Person 2 predicts 'e', Person 1 has to report that the character 'e' is present at position 2
2. If the character is not present, Person 1 simply reports that the character is not present in his/her word.

Refer to this Wikipedia article for more details.

1. The main model consists of a RNN which acts as an encoder. It takes the encoded version of the incomplete string and returns the hidden state at the last time step of the last layer.
2. On the other hand, we take the missed characters, encode it into a vector and pass it through a linear layer.
3. The outputs from 1 and 2 are concatenated and we pass this concatenated vector through a hidden layer and map it to a final layer with number of neurons = size of vocabulary (26 in our case for the English alphabet).

The model is trained using Binary Cross Entropy Loss since it is a multi-label classification problem. A pretrained model can be found here. It is a 2-layer, 512 hidden unit GRU with dropout and trained using Adam.

The model is trained on a corpus of around 227k English words which can be found in the datasets folder. Testing is done on a corpus of 20k words. There is a 50% overlap between the training dataset and the testing dataset.

After preliminary testing, here is a plot of the average number of misses vs length of the word: As you can observe, the average misses decreases as length of the word decreases. This makes sense intuitively since longer the word -> chances of higher number of unique characters increases -> chances of a predicted chaarcter not being present decreases.

1. Weight examples during training with weights inversely proportional to length.