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Text representation can aid machines in understanding text. Previous work ontext representation often focuses on the so-called forward implication, i.e.,preceding words are taken as the context of later words for creatingrepresentations, thus ignoring the fact that the semantics of a text segment isa product of the mutual implication of words in the text: later wordscontribute to the meaning of preceding words. We introduce the concept ofinteraction and propose a two-perspective interaction representation, thatencapsulates a local and a global interaction representation. Here, a localinteraction representation is one that interacts among words withparent-children relationships on the syntactic trees and a global interactioninterpretation is one that interacts among all the words in a sentence. Wecombine the two interaction representations to develop a Hybrid InteractionRepresentation (HIR).  Inspired by existing feature-based and fine-tuning-based pretrain-finetuningapproaches to language models, we integrate the advantages of feature-based andfine-tuning-based methods to propose the Pre-train, Interact, Fine-tune (PIF)architecture.  We evaluate our proposed models on five widely-used datasets for textclassification tasks. Our ensemble method, outperforms state-of-the-artbaselines with improvements ranging from 2.03% to 3.15% in terms of error rate.In addition, we find that, the improvements of PIF against moststate-of-the-art methods is not affected by increasing of the length of thetext.

Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification