埋め込み・言語モデル
11 の手法がこの系統にあります。
注目
自動テキスト評価Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generaBERT埋め込みBERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. BecaNLPにおける対照学習Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that traiDoc2VecDoc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectNLPにおけるジェンダーバイアス検出Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora GloVe埋め込みGloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global wo
学びの道筋
このトピックで最も多く参照される基礎的な手法を、発展してきた順に並べました — はじめての方はここから読み始めてください。