Abstract: The disclosure relates to a method for training a machine reading comprehension model, a computer-readable recording medium storing the method, and a question answering system using a trained machine reading comprehension model. The method for training a machine reading comprehension model includes: obtaining features of input sentences; generating an input sequence including a plurality of tokens based on at least one feature among the obtained features; embedding the input sequence using a pre-trained language model; inputting an output embedding, obtained by embedding the input sequence, to a first encoding layer to perform word-level encoding; and inputting a portion of hidden states of the first encoding layer to a second encoding layer to perform sentence-level encoding.

Abstract: A method of training a part-of-speech (POS) tagging model includes: separating an input sentence into units of syllables to generate an input sequence; encoding, using at least one encoder included in a part-of-speech (POS) tagging model, the input sequence; generating, based on the encoded input sequence and using a first discriminator included in the POS tagging model, a POS tagging result; and generating, based on the encoded input sequence and using a second discriminator included in the POS tagging model, a spacing result.

Abstract: Disclosed herein is a system including an answer determination module configured to analyze an input sentence to determine whether to answer the input sentence, a learning module configured to output a domain corresponding to the input sentence and a plurality of categories to which the input sentence belongs when it is determined to answer the input sentence, and an output module configured to output an answer to the input sentence, wherein the learning module performs multi-task learning using the input sentence as input data and using as output data the domain corresponding to the input sentence and the plurality of categories to which the input sentence belongs.

Abstract: A method for training a slot tagging model may, when an entity used for slot tagging is added, accurately perform slot tagging corresponding to the added entity only by adding new data to an external dictionary, without retraining, a computer-readable medium storing a program for performing the training method, a speech recognition apparatus providing a speech recognition service using the trained slot tagging model, and an electronic device used to provide the speech recognition service.

Abstract: Provided is a named entity recognition system, including: an input module configured to recognize a speech input of a user and convert the speech input into text; a preprocessing module configured to separate the text in units of syllables and perform transformation; and a learning module configured to perform multi-task learning for recognizing a named entity and identifying a boundary of spacing with respect to the transformed text, and output a result of recognizing the named entity and a result of identifying the boundary of spacing, based on recognizing the named entity and identifying the boundary of spacing.

Abstract: A question-and-answer system providing an appropriate answer for a vehicle- related FAQ by using deep learning, and corresponding method are provided. The question-and-answer system includes: a memory that stores a plurality of representative questions to match a plurality of answers corresponding to the plurality of representative questions, respectively; a learning module configured to output a representative question corresponding to an input sentence from among the stored plurality of representative questions; and an output module configured to search the memory or an answer that matches the output representative question and output the found answer.