**Review-guided Answer Helpfulness Prediction** (RAHP) is a textual inference model for identifying helpful answers in e-commerce. It not only considers the interactions between QA pairs, but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.

A novel low-resource intrinsic metric to evaluate word
embedding quality based on graph modularity.

Categorical Modularity

Evaluating Word Embeddings with Categorical Modularity

RAHP

Review-guided Helpful Answer Identification in E-commerce

HBMP is a hierarchy-like structure of [BiLSTM](https://paperswithcode.com/method/bilstm) layers with [max pooling](https://paperswithcode.com/method/max-pooling). All in all, this model improves the previous state of the art for SciTail and achieves strong results for the SNLI and MultiNLI.

Source	Review-guided Helpful Answer Identification in E-commerce
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com