Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity
This work addresses the challenge of accurately measuring sentence similarity for NLP applications, representing an incremental improvement over existing methods.
The paper tackles the problem of semantic textual similarity by proposing a model that uses multiple word embeddings and multi-level comparison to generate sentence embeddings, achieving state-of-the-art performance on STS Benchmark and SICK datasets.
Different word embedding models capture different aspects of linguistic properties. This inspired us to propose a model (M-MaxLSTM-CNN) for employing multiple sets of word embeddings for evaluating sentence similarity/relation. Representing each word by multiple word embeddings, the MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). According to the experimental results on STS Benchmark dataset and SICK dataset from SemEval, M-MaxLSTM-CNN outperforms the state-of-the-art methods for textual similarity tasks. Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension.