RCE-HIL: Recognizing Cross-media Entailment with Heterogeneous Interactive Learning

Abstract

Entailment recognition is an important paradigm of reasoning that judges if a hypothesis can be inferred from given premises. However, previous efforts mainly concentrate on text-based reasoning as recognizing textual entailment (RTE), where the hypotheses and premises are both textual. In fact, humans' reasoning process has the characteristic of cross-media reasoning. It is naturally based on the joint inference with different sensory organs, which represent complementary reasoning cues from unique perspectives as language, vision, and audition. How to realize cross-media reasoning has been a significant challenge to achieve the breakthrough for width and depth of entailment recognition. Therefore, this article extends RTE to a novel reasoning paradigm: recognizing cross-media entailment (RCE), and proposes heterogeneous interactive learning (HIL) approach. Specifically, HIL recognizes entailment relationships via cross-media joint inference, from image-text premises to text hypotheses. It is an end-to-end architecture with two parts: (1) Cross-media hybrid embedding is proposed to perform cross embedding of premises and hypotheses for generating their fine-grained representations. It aims to achieve the alignment of cross-media inference cues via image-text and text-text interactive attention. (2) Heterogeneous joint inference is proposed to construct a heterogeneous interaction tensor space and extract semantic features for entailment recognition. It aims to simultaneously capture the interaction between cross-media premises and hypotheses and distinguish their entailment relationships. Experimental results on widely used Stanford natural language inference (SNLI) dataset with image premises from Flickr30K dataset verify the effectiveness of HIL and the intrinsic inter-media complementarity in reasoning.