15. A non-transitory, computer-readable medium having instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising: receiving a speech input representing a question; converting the speech input to a string of characters in a natural language; obtaining tokens corresponding to a string of characters in a natural language, each token representing a potential word including at least one character of the string of characters; determining one or more part-of-speech (POS) tags for each token; determining sequences of the POS tags for the tokens, each sequence of the POS tags including one POS tag per token; determining one or more parses for each sequence of the POS tags for the tokens; determining a most-likely parse and its corresponding sequence of the POS tags for the tokens to obtain a selected parse and a selected sequence of the POS tags for the tokens by solving a maximum-a-posteriori (MAP) inference problem defined as: ( x * , y * ) = arg ⁢ ⁢ max x ∈ X , y ∈ Y ⁢ ⁢ θ T T ⁢ F ( w ) ⁢ x + θ P T ⁢ G ( x , w ) ⁢ y , where x* and y* represent a specific sequence of the POS tags and a specific parse, respectively, X represents a set of the POS tags x, Y represents a set of the parses y, θ T T represents a transformed tagging weight vector, F (w) represents a tagging feature matrix, θ P T represents a transformed parsing weight vector, and G (x,w) represents a parsing feature matrix; determining a most-likely answer to the question using the selected parse and the selected sequence of the POS tags for the tokens; and outputting the most-likely answer.
15. A non-transitory, computer-readable medium having instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising: receiving a speech input representing a question; converting the speech input to a string of characters in a natural language; obtaining tokens corresponding to a string of characters in a natural language, each token representing a potential word including at least one character of the string of characters; determining one or more part-of-speech (POS) tags for each token; determining sequences of the POS tags for the tokens, each sequence of the POS tags including one POS tag per token; determining one or more parses for each sequence of the POS tags for the tokens; determining a most-likely parse and its corresponding sequence of the POS tags for the tokens to obtain a selected parse and a selected sequence of the POS tags for the tokens by solving a maximum-a-posteriori (MAP) inference problem defined as: ( x * , y * ) = arg ⁢ ⁢ max x ∈ X , y ∈ Y ⁢ ⁢ θ T T ⁢ F ( w ) ⁢ x + θ P T ⁢ G ( x , w ) ⁢ y , where x* and y* represent a specific sequence of the POS tags and a specific parse, respectively, X represents a set of the POS tags x, Y represents a set of the parses y, θ T T represents a transformed tagging weight vector, F (w) represents a tagging feature matrix, θ P T represents a transformed parsing weight vector, and G (x,w) represents a parsing feature matrix; determining a most-likely answer to the question using the selected parse and the selected sequence of the POS tags for the tokens; and outputting the most-likely answer. 17. The computer-readable medium of claim 15 , wherein the operations further include tokenizing the string of characters to obtain the tokens.