61 citations · 133 across the 10 of their papers we have counts for
5 papers · 1 filter
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding
Zhao Jinman, Shawn Zhong, Xiaomin Zhang +1
We look into the task of \emph{generalizing} word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-…
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer
Siddhant Garg, Rohit Kumar Sharma, Yingyu Liang
Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. In this paper we show that concatenating the embeddings from the pre-tr…
Generalizing Word Embeddings using Bag of Subwords
Jinman Zhao, Sidharth Mudgal, Yingyu Liang
We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word…
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang +3
Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textua…
Domain Adapted Word Embeddings for Improved Sentiment Classification
Prathusha K Sarma, YIngyu Liang, William A Sethares
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a m…