Sentence Pair Modeling
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Greatest papers with code
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
• sinovation/ZEN
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Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
CHINESE NAMED ENTITY RECOGNITION CHINESE WORD SEGMENTATION DOCUMENT CLASSIFICATION NATURAL LANGUAGE INFERENCE PART-OF-SPEECH TAGGING SENTENCE PAIR MODELING SENTIMENT ANALYSIS
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
COLING 2018
• lanwuwei/SPM_toolkit
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In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks.
NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING
Character-based Neural Networks for Sentence Pair Modeling
NAACL 2018
• lanwuwei/SPM_toolkit
•
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.
NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks.