Various Techniques of Sentence Embedding using Question-Answering in Game Play
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (37)
- Medved, Marek, "Sentence and Word Embedding Employed in Open Question-Answering", ICAART, (2), pp.486~492 (2018)
- Li, Bohan, "On the Sentence Embeddings from BERT for Semantic Textual Similarity", Proceedings of the 2020Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.9119~9130 (2020)
- S. Minaee, "Automatic question-answering using a deep similarity neural network", 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp.923~927 (2017)
- De Boni, Marco, "New Directions in Question Answering", pp.138~144 (2003)
- Green Jr, Bert F, "Baseball: an automatic question-answerer", Papers presented at the May 9-11, 1961, western joint IRE-AIEE-ACM computer conference, pp.219~224 (1961)
- Zhu, Fengbin, "Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering", ~ (2021)
- Chen, Danqi, "Reading wikipedia to answer open-domain questions", ~ (2017)
- Voorhees, Ellen M, "The TREC-8 question answering track report", Trec, 99, pp.77~82 (1999)
- Mihaylov, Todor, "Can a suit of armor conduct electricity? a new dataset for open book question answering", ~ (2018)
- Bengio, Yoshua, "A neural probabilistic language model", The journal of machine learning research, 3, pp.1137~1155 (2003)
- Zhou, Junru, "LIMIT-BERT: Linguistic informed multi-task bert", ~ (2019)
- Levy, Omer, "Dependency-based word embeddings", Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2, pp.302~308 (2014)
- Melamud, Oren, "Context2vec: Learning generic context embedding with bidirectional lstm", Proceedings of the 20th SIGNLL conference on computational natural language learning, pp.51~61 (2016)
- Gardner, Matt, "Allennlp: A deep semantic natural language processing platform", ~ (2018)
- Devlin, Jacob, "Bert: Pre-training of deep bidirectional transformers for language understanding", ~ (2018)
- V. C. John Burger, "Issues, tasks and program structures to roadmap research in question & answering", Document Understanding Conferences Roadmapping Documents, pp.1~35 (2001)
- O. Kolomiyets, "A survey on question answering technology from an information retrieval perspective", Inf. Sci, 181(24), pp.5412~5434 (2011)
- A. Allam, "The question answering systems: A survey", International Journal of Research and Reviews in Information Sciences, pp.211~221 (2012)
- A. Mishra, "A survey on question answering systems with classification", J. King Saud Univ. Comput. Inf. Sci, 28(3), pp.345~361 (2016)
- M. Pasca, "Open-domain question answering from large text collections", Computational Linguistics, 29(4), pp.665~667 (2003)
- Z. Huang, "Recent trends in deep learning based opendomain textual question answering systems", IEEE Access, 8, pp.94341~94356 (2020)
- T. Lei, "A novel cnnbased method for question classification in intelligent question answering", Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, Association for Computing Machinery (2018)
- W. Xia, "Novel architecture for long short-term memory used in question classification", Neurocomputing, 299, pp.20~31 (2018)
- K. Nishida, "Retrieve-and-read: Multi-task learning of information retrieval and reading comprehension", Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ser. CIKM ’18. Association for Computing Machinery, pp.647~656 (2018)
- V. Karpukhin, "Dense passage retrieval for open-domain question answering", ~ (2020)
- "Relevance-guided Supervision for OpenQA with ColBERT" (2020)[URL]
- K. M. Hermann, "Teaching machines to read and comprehend", Proceedings of the 28th International Conference on Neural Information Processing Systems, 1, pp.1693~1701 (2015), MIT Press
- P. Rajpurkar, "SQuAD: 100,000+ questions for machine comprehension of text", Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp.2383~2392 (2016)
- T. Nguyen, "MS MARCO: A human generated machine reading comprehension dataset", CoRR (2016)
- G. Lai, "RACE: largescale reading comprehension dataset from examinations", CoRR (2017)
- P. Rajpurkar, "Know what you don’t know: Unanswerable questions for SQuAD", Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2, pp.784~789 (2018), Association for Computational Linguistics
- R. Das, "Multi-step retriever-reader interaction for scalable open-domain question answering", International Conference on Learning Representations (2019)
- Budiharto, W, "Deep learning-based question answering system for intelligent humanoid robot", J Big Data, 7, pp.77~ (2020)[DOI]
- Sasikumar, Unmesh, "A survey of natural language question answering system", International Journal of Computer Applications, 108(15), pp.975~8887 (2014)
- "NLP–Building a Question-Answering model"[URL]
- Rifqi Muhammad Fikri, "Developing Machine Learning-based Mobile Application to Identify Ingredients of Korean Street Food", 아시아태평양융합연구교류논문지, 6(6), pp.1~11 (2020), 사단법인 한국융합기술연구학회[DOI]