STS

In this paper we present our systems for the STS task. Our systems are all based on a simple process of identifying the components that correspond between two sentences. Cur- …...

We represent natural language semantics by combining logical and distributional in- formation in probabilistic logic. We use Markov Logic Networks (MLN) for the RTE task, and Probabilistic Soft Logic (PSL) …...

We experiment with learning word representa- tions designed to be combined into sentence- level semantic representations, using an ob- jective function which does not directly make use of the supervised …...

AbstractMultilingual sentence embeddings capture rich semantic information not only for measuring similarity between texts but also for catering to a broad range of downstream cross-lingual NLP tasks. State-of-the-art multilingual sentence …...

AbstractThis paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself …...

AbstractLearning high-quality sentence representations is a fundamental problem of natural language processing which could benefit a wide range of downstream tasks. Though the BERT-like pre-trained language models have achieved great …...

AbstractThis paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE), which combines contrastive learning with a novel shuffled group whitening. Generally, contrastive learning pulls distortions of a …...