Abstract: A machine reading comprehension apparatus and method are provided. The apparatus receives a question and a text. The apparatus generates a plurality of first predicted answers and a plurality of first source sentences corresponding to each of the first predicted answers according to the question, the text, and the machine reading comprehension model. The apparatus determines a question category of the question. The apparatus extracts a plurality of special terms related to the question category and a plurality of second source sentences corresponding to each of the special terms from the text. The apparatus concatenates the question, the first source sentences, the second source sentences, the first predicted answers, and the special terms into an extended string. The apparatus generates a plurality of second predicted answers corresponding to the question according to the extended string and the micro finder model.

Abstract: A machine reading comprehension method includes the following operations: performing a relation augment self attention (RASA) feature extraction process on at least one historical dialogue data and a current question data respectively to obtain at least one historical dialogue feature and a current question feature; and performing a machine reading comprehension (MRC) analysis according to the at least one historical dialogue feature and the current question feature to obtain a response output.

Abstract: A machine reading comprehension method is disclosed. The machine reading comprehension method includes the following operations: performing a relation augment self attention (RASA) feature extraction process on at least one historical dialogue data and a current question data respectively to obtain at least one historical dialogue feature and a current question feature; and performing a machine reading comprehension (MRC) analysis according to the at least one historical dialogue feature and the current question feature to obtain a response output.

Abstract: An apparatus and method for generating a dialogue state tracking model. The apparatus retrieves a field feature corresponding to a queried field from a database according to the queried field corresponding to a queried message. The apparatus retrieves a candidate-term feature corresponding to each of at least one candidate-term corresponding to the queried field from the database, and integrates them into an integrated feature. The apparatus also generates at least one relation sub-sentence of a reply message corresponding to the queried message and generates a sentence relation feature according to the at least one relation sub-sentence. The apparatus further generates a queried field related feature according to the field feature, the integrated feature and the sentence relation feature and trains the dialogue state tracking model according to the queried field related feature.

Abstract: An apparatus and method for generating a dialogue state tracking model. The apparatus retrieves a field feature corresponding to a queried field from a database according to the queried field corresponding to a queried message. The apparatus retrieves a candidate-term feature corresponding to each of at least one candidate-term corresponding to the queried field from the database, and integrates them into an integrated feature. The apparatus also generates at least one relation sub-sentence of a reply message corresponding to the queried message and generates a sentence relation feature according to the at least one relation sub-sentence. The apparatus further generates a queried field related feature according to the field feature, the integrated feature and the sentence relation feature and trains the dialogue state tracking model according to the queried field related feature.