A context-aware chatbot method and system are provided. The context-aware chatbot method comprises receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user. The context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A context-aware chatbot method, comprising: receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user, wherein the context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors. 2. The context-aware chatbot method according to claim 1, wherein determining a question type of the question to be answered further including: identifying a Lexical Answer Type (LAT) of the question to be answered. 3. The context-aware chatbot method according to claim 1, wherein generating at least one answer to the question based on a context-aware neural conversation model further including: provided an input sentence X={x1, x2, . . . , xn}, finding a response sentence Y={y1, y2, . . . , yn} by taking a context EC={ec1, ec2, . . . , ec,} into consideration, wherein x represents a word in the input sentence, y represents a word in the response sentence, the response sentence Y represents the answer, and the input sentence X represents the question to be answered. 4. The context-aware chatbot method according to claim 3, wherein provided an input sentence X={x1, x2, . . . , xn}, finding a response sentence Y={y1, y2, . . . , yn} by taking a context EC={ec1, ec2, . . . , ecm} into consideration further including: predicting y by maximizing a probability P (yt|yt−1, . . . , y1,ec). 5. The context-aware chatbot method according to claim 4, wherein predicting y by maximizing a probability P (yt|yt−1, . . . , y1, ec) further including: providing the input sentence with an input gate it, a memory gate ft, and an output gate ot, by the context-aware neural conversation model; and calculating a vector representation ht for each time step t by: 〚 i t f t o t l t 〛 = 〚 θ θ θ tanh 〛  W * 〚 h t - 1 x t s 〛 c t = f t   c t - 1 + i t  l t h t s = o t * tanh  ( c t ) , where W = 〚 W i W t W o W l 〛 , where Wi, Wf, Wo, Wl∈RK*2K, W denotes learned and trained factors, xt denotes a vector representation for an individual word at time step t, ht denotes a vector representation computed by Long Short Term Memory (LSTM) model at the time step t by combining xt and ht−1, ct denotes a cell state vector representation at time step t, and θ denotes a sigmoid function. 6. The context-aware chatbot method according to claim 5, further including: calculating a distribution over outputs and sequentially predicted tokens based on a softmax function: p  ( Y | X ) = ∏ t = 1 n y   p  ( y t | x 1 , x 2 , …  , x t , y 1 , …  , y t - 1 ) = ∏ t = 1 n y  exp  ( f  ( h t - 1 , e yt ) ) ∑ y ′   exp  ( f  ( h t - 1 , e y ′ ) ) , where f (ht−1, eyt) denotes an activation function between ht−1 and eyt . 7. The context-aware chatbot method according to claim 6, wherein: terminating a decoding of the input sentence when an EOS token is predicted. 8. The context-aware chatbot method according to claim 1, wherein validating the answer generated by the context-aware neural conversation model further including: calculating a confidence score for the answer generated by the context-aware neural conversation model, wherein the confidence score is a normalized Kullback-Leibler distance between the question and the answer. 9. A non-transitory computer-readable medium having computer program for, when being executed by a processor, performing a context-aware chatbot method, the method comprising: receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user, wherein the context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors. 10. The non-transitory computer-readable medium according to claim 9, wherein determining a question type of the question to be answered further including: identifying a Lexical Answer Type (LAT) of the question to be answered. 11. The non-transitory computer-readable medium according to claim 9, wherein generating at least one answer to the question based on a context-aware neural conversation model further including: given an input sentence X={x1, x2, . . . , xn}, finding a response sentence Y={y1, y2, . . . , yn} by taking a context EC={ec1, ec2, . . . , ecm} into consideration, where x represents a word in the input sentence, y represents a word in the response sentence, the response sentence Y represents the answer, and the input sentence X represents the question to be answered. 12. The non-transitory computer-readable medium according to claim 11, wherein given an input sentence X={x1, x2, . . . , xn}, finding a response sentence Y={y1, y2, . . . , yn} by taking a context EC={ec1, ec2, . . . , ecm} into consideration further including: predicting y by maximizing a probability P (yt|yt−1, . . . , y1, ec). 13. The non-transitory computer-readable medium according to claim 12, wherein predicting y by maximizing a probability P (yt|yt−1, . . . , y1, ec) further including: providing the input sentence with an input gate it, a memory gate ft, and an output gate ot, by the context-aware neural conversation model; calculating a vector representation ht for each time step t by: 〚 i t f t o t l t 〛 = 〚 θ θ θ tanh 〛  W * 〚 h t - 1 x t s 〛 c t = f t   c t - 1 + i t  l t h t s = o t * tanh  ( c t ) , where W = 〚 W i W t W o W l 〛 , where Wi, Wf, Wo, Wl∈RK*2K, W denotes learned and trained factors, xt denotes a vector representation for an individual word at time step t, ht denotes a vector representation computed by Long Short Term Memory (LSTM) model at the time step t by combining xt and ht−1, ct denotes a cell state vector representation at time step t, and θ denotes a sigmoid function, and calculating a distribution over outputs and sequentially predicted tokens based on a softmax function p  ( Y | X ) = ∏ t = 1 n y   p  ( y t | x 1 , x 2 , …  , x t , y 1 , …  , y t - 1 ) = ∏ t = 1 n y  exp  ( f  ( h t - 1 , e yt ) ) ∑ y ′   exp  ( f  ( h t - 1 , e y ′ ) ) , where f (ht−1, eyt) denotes an activation function between ht−1 and eyt. 14. The non-transitory computer-readable medium according to claim 9, wherein validating the answer generated by the context-aware neural conversation model further including: calculating a confidence score for the answer generated by the context-aware neural conversation model, wherein the confidence score is a normalized Kullback-Leibler distance between the question and the answer. 15. A context-aware chatbot system, comprising: a question acquisition module configured to receive a user's voice and convert the user's voice to a question to be answered; a question determination module configured to determine a question type of the question to be answered; a context-aware neural conversation module configured to generate at least one answer to the question by taking contextual information of the question into consideration and decomposing the contextual information of the question into a plurality of high dimension vectors; an evidence validation module configured to validate the answer generated by the context-aware neural conversation model; and an answer delivery module configured to deliver the answer validated to the user. 16. The context-aware chatbot system according to claim 15, wherein the question determination module is configured to: identify a Lexical Answer Type (LAT) of the question to be answered. 17. The context-aware chatbot system according to claim 15, wherein the context-aware neural conversation module is configured to: given an input sentence X={x1, x2, . . . , xn}, find a response sentence Y={y1, y2, . . . , yn} by taking a context EC={ec1, ec2, . . . , ecm} into consideration, where x represents a word in the input sentence, y represents a word in the response sentence, the response sentence Y represents the answer, and the input sentence X represents the question to be answered. 18. The context-aware chatbot system according to claim 17, wherein the context-aware neural conversation module is configured to: predict y by maximizing a probability P (yt,|yt−1, . . . , y1, ec). 19. The context-aware chatbot system according to claim 18, wherein the context-aware neural conversation module is configured to: provide the input sentence with an input gate it, a memory gate ft, and an output gate ot, by the context-aware neural conversation model; calculate a vector representation ht for each time step t by: 〚 i t f t o t l t 〛 = 〚 θ θ θ tanh 〛  W * 〚 h t - 1 x t s 〛 c t = f t   c t - 1 + i t  l t h t s = o t * tanh  ( c t ) , where W = 〚 W i W t W o W l 〛 , where Wi, Wf, Wo, Wl∈RK*2K,W denotes learned and trained factors, xt denotes a vector representation for an individual word at time step t, ht denotes a vector representation computed by Long Short Term Memory (LSTM) model at the time step t by combining xt and ht−1, ct denotes a cell state vector representation at time step t, and θ denotes a sigmoid function, and calculate a distribution over outputs and sequentially predicted tokens based on a softmax function p  ( Y | X ) = ∏ t = 1 n y   p  ( y t | x 1 , x 2 , …  , x t , y 1 , …  , y t - 1 ) = ∏ t = 1 n y  exp  ( f  ( h t - 1 , e yt ) ) ∑ y ′   exp  ( f  ( h t - 1 , e y ′ ) ) , where f (ht−1, eyt) denotes an activation function between ht−1 and eyt. 20. The context-aware chatbot system according to claim 15, wherein the evidence validation module is further configured to: calculate a confidence score for the answer generated by the context-aware neural conversation model, wherein the confidence score is a normalized Kullback-Leibler distance between the question and the answer.