CONF Mai_ICLR2019_2019/IDIAP CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model Mai, Florian Galke, Lukas Scherp, Ansgar Efficient training scheme Sentence embedding Text representation learning word2vec http://publications.idiap.ch/index.php/publications/showcite/Mai_Idiap-RR-06-2019 Related documents International Conference on Learning Representations New Orleans, Louisiana, USA 2019 https://openreview.net/forum?id=H1MgjoR9tQ URL Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%. REPORT Mai_Idiap-RR-06-2019/IDIAP CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model Mai, Florian Galke, Lukas Scherp, Ansgar Efficient training scheme Sentence embedding Text representation learning word2vec EXTERNAL http://publications.idiap.ch/attachments/reports/2019/Mai_Idiap-RR-06-2019.pdf PUBLIC Idiap-RR-06-2019 2019 Idiap July 2019 To appear at ICLR 2019 Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ and ZYX are the same. In order to address this shortcoming, we propose a learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of word2vec, so that it can be trained on large quantities of unlabeled text. We empirically show that CMOW better captures linguistic properties, but it is inferior to CBOW in memorizing word content. Motivated by these findings, we propose a hybrid model that combines the strengths of CBOW and CMOW. Our results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to memorize word content while at the same time substantially improving its ability to encode other linguistic information by 8%. As a result, the hybrid also performs better on 8 out of 11 supervised downstream tasks with an average improvement of 1.2%. https://openreview.net/forum?id=H1MgjoR9tQ URL