Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 20201st edition The Editor(s) (if

铁砧

Outlook,consider the following directions including using more unsupervised data, utilizing few labeled data, employing deeper neural architectures, improving model interpretability and fusing the advantages of other areas.

放弃

nauseate

lactic

Compositional Semantics,erefore, compositional semantics has remained a core task in NLP. In this chapter, we first introduce various models for binary semantic composition, including additive models and multiplicative models. After that, we present various typical models for N-ary semantic composition including recurrent

ENNUI

Sentence Representation,use many important applications in related fields lie on understanding sentences, for example, summarization, machine translation, sentiment analysis, and dialogue system. Sentence representation aims to encode the semantic information into a real-valued representation vector, which will be utilized

厨房里面

Intersect

Gerontology

拖债

Cross-Modal Representation,s including texts, audio, images, videos, etc. In this chapter, we first introduce typical cross-modal representation models. After that, we review several real-world applications related to cross-modal representation learning including image captioning, visual relation detection, and visual questio

蚊子