Sentence Pair Modeling

5 papers with code • 0 benchmarks • 0 datasets

Comparing two sentences and their relationship based on their internal representation.

Most implemented papers

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations

sinovation/ZEN Findings of the Association for Computational Linguistics 2020

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.

Character-based Neural Networks for Sentence Pair Modeling

lanwuwei/SPM_toolkit NAACL 2018

Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

lanwuwei/SPM_toolkit COLING 2018

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.

Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding

microsoft/Distilled-Sentence-Embedding 14 Aug 2019

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.

Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks

UKPLab/sentence-transformers NAACL 2021

Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.