The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction. We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. We show that such compositions are not expressive enough for several tasks (e.g. In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). concatenation or sum) of those vectors as features for the prediction. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. Various NLP problems вЂ“ such as the prediction of sentence similarity, entailment, and discourse relations вЂ“ are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. We show via experiments that by combining feature engineering with embedding learning, our method can outperform CBOW using only 10% of the training data in both the standard word embedding evaluations and also text classification experiments.Д¬omposition of Sentence Embeddings: Lessons from Statistical Relational Learning Indeed, standard models such as CBOW and fasttext are specific choices along each of these axes. Our framework is based on the well-studied problem of multi-label classification and, consequently, exposes several design choices for featurizing words and contexts, loss functions for training and score normalization. We define a new modeling framework for training word embeddings that captures this intuition. In this paper, we argue that, despite the successes of this assumption, it is incomplete: in addition to its context, orthographical or morphological aspects of words can offer clues about their meaning. Most word embeddings today are trained by optimizing a language modeling goal of scoring words in their context, modeled as a multi-class classification problem.

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