Various Techniques of Sentence Embedding using Question-Answering in Game Play

서지정보

ㆍ발행기관 : 한국컴퓨터게임학회 ㆍ수록지정보 : 한국컴퓨터게임학회 논문지 / 35권 / 4호
ㆍ저자명 : Trieu Thanh Ngoan, Won-Hyung LEE

목차

ABSTRACT
1. Introduction
2. Related Research Work
3. PROPOSED METHODOLOGY
4. EXPERIMENTAL ANALYSIS ANDCOMPARISON OF THE SENTENCEEMBEDDING MODEL
5. Conclusion
Acknowledgemnt
Reference

영어 초록

Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. The need to query information content available in various formats including structured and unstructured data has become increasingly important. Thus, Question Answering Systems (QAS) are essential to satisfy this need. QAS aim at satisfying users who are looking to answer a specific question in natural language.Moreover, it is a representative of open domain QA systems, where the answer selection process leans on syntactic and semantic similarities between the question and the answering text snippets. Such approach is specifically oriented to languages with fine grained syntactic and morphologic features that help to guide the correct QA match. Furthermore, word and sentence embedding have become an essential part of any Deep-Learning-based natural language processing systems as they encode words and sentences in fixed-length dense vectors to drastically improve the processing of textual data. The paper will concentrate on incorporating the sentence embedding with its various techniques like Infersent, ElMo and BERT in the construction of Question Answering systems, and also it can be used in game play.

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