Analysis of Machine Translation Systems\u27 Errors in Tense, Aspect, and Modality

PACLIC 19 / Taipei, taiwan / December 1-3, 200

言語学的特徴を用いた述部の正規化と同義性判定

京都大学0048新制・課程博士博士(情報学)甲第17991号情博第513号新制||情||91(附属図書館)80835京都大学大学院情報学研究科知能情報学専攻(主査)教授 黒橋 禎夫, 教授 石田 亨, 教授 河原 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Memory-Based Learning of Word Translation

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 231-234

QNRs: toward language for intelligent machines

Impoverished syntax and nondifferentiable vocabularies make natural language a poor medium for neural representation learning and applications. Learned, quasilinguistic neural representations (QNRs) can upgrade words to embeddings and syntax to graphs to provide a more expressive and computationally tractable medium. Graph-structured, embedding-based quasilinguistic representations can support formal and informal reasoning, human and inter-agent communication, and the development of scalable quasilinguistic corpora with characteristics of both literatures and associative memory. To achieve human-like intellectual competence, machines must be fully literate, able not only to read and learn, but to write things worth retaining as contributions to collective knowledge. In support of this goal, QNR-based systems could translate and process natural language corpora to support the aggregation, refinement, integration, extension, and application of knowledge at scale. Incremental development of QNRbased models can build on current methods in neural machine learning, and as systems mature, could potentially complement or replace today’s opaque, error-prone “foundation models” with systems that are more capable, interpretable, and epistemically reliable. Potential applications and implications are broad