1. A method for providing response message completion, comprising: determining a response completion model (RCM) using a linear combination of a generic response language model (LM) and a stimulus model with a mixing parameter for a mixing model, the mixing parameter being based, at least in part, on a topic probability, the topic probability to be assigned a value based, at least in part, on a likelihood that a candidate topic is associated with a received stimulus message; receiving a stimulus message (SM); if an incomplete response message includes at least one preceding word, determining at least one candidate next word for the incomplete response message based, at least in part, on the RCM, the SM, and the at least one preceding word and based, at least in part, on estimated word frequencies in stimulus messages from a plurality of stimulus-response message pairs; selecting at least one word from the at least one determined candidate next word; including the selected at least one word within the incomplete response message; and generating a complete response message that is based, at least in part, on the incomplete response message that includes the selected at least one word.
1. A method for providing response message completion, comprising: determining a response completion model (RCM) using a linear combination of a generic response language model (LM) and a stimulus model with a mixing parameter for a mixing model, the mixing parameter being based, at least in part, on a topic probability, the topic probability to be assigned a value based, at least in part, on a likelihood that a candidate topic is associated with a received stimulus message; receiving a stimulus message (SM); if an incomplete response message includes at least one preceding word, determining at least one candidate next word for the incomplete response message based, at least in part, on the RCM, the SM, and the at least one preceding word and based, at least in part, on estimated word frequencies in stimulus messages from a plurality of stimulus-response message pairs; selecting at least one word from the at least one determined candidate next word; including the selected at least one word within the incomplete response message; and generating a complete response message that is based, at least in part, on the incomplete response message that includes the selected at least one word. 7. The method of claim 1 , wherein determining at least one candidate next word further comprises: determining at least one LM probability for a plurality of words in a dictionary based, at least in part, on the at least one determined preceding word within the incomplete response message; determining the topic probability for a plurality of candidate topics based, at least in part, on a topic model and the SM; selecting at least one topic based, at least in part, on the determined plurality of candidate topic probabilities; determining at least one stimulus probability for the plurality of dictionary word based, at least in part on the selected at least one topic; ranking the plurality of dictionary words based, at least in part on the at least one LM probability, the at least one determined stimulus probability, and the mixing parameter; and determining the at least one candidate word for the incomplete response message based, at least in part, on the ranked plurality of dictionary words.