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Search : [ author: 김지희 ] (2)

Type-specific Multi-Head Shared-Encoder Model for Commonsense Machine Reading Comprehension

Jinyeong Chae, Jihie Kim

http://doi.org/10.5626/JOK.2023.50.5.376

Machine reading comprehension (MRC) is a task introduced to a machine that can understand natural languages by solving various tasks based on given context. To evaluate natural language understanding of machine, a machine must make commonsense inference under full comprehension of a given context. To enhance model obtaining such abilities, we proposed a multi-task learning scheme and a model for commonsense MRC. Contributions of this study are as follows: 1) a method of task-specific dataset configuration is proposed; 2) a type-specific multi-head shared-encoder model with multi-task learning scheme including batch sampling and loss scaling is developed; and 3) when the method is evaluated on CosmosQA dataset (commonsense MRC), the accuracy was improved by 2.38% compared to the performance at baseline with fine-tuning.

RNN model for Emotion Recognition in Dialogue by incorporating the Attention on the Other’s State

Seunguook Lim, Jihie Kim

http://doi.org/10.5626/JOK.2021.48.7.802

Emotion recognition has increasingly received much attention in artificial intelligence, lately. In this paper, we present an RNN model that analyzes and identifies a speaker’s emotion appeared through utterances in conversation. There are two kinds of speaker considered context, self-dependency and inter-speaker dependency. In particular, we focus more on inter-speaker dependency by considering that the state context information of the relative speaker can affect the emotions of the current speaker. We propose a DialogueRNN based model that adds a new GRU Cell for catching inter-speaker dependency. Our model shows higher performance than the performances of DialogueRNN and its three variants on multiple emotion classification datasets.


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