Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models
نویسندگان
چکیده
Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation. One of main challenges logic-based approaches to this task lack background knowledge. This study proposes a logical inference system with phrasal knowledge by comparing their visual representations based on intuition that enable people judge entailment relations. First, we obtain candidate phrase pairs for from inference. Second, using vision-and-language model, acquire these phrases form images or embedding vectors. Finally, compare obtained determine whether inject corresponding candidate. In addition simple similarity between phrases, also consider asymmetric relations when representations. Our improved accuracy SICK dataset compared previous system, SPSA (Selector Predicates Shared Arguments). Moreover, our evaluation functions models are effective at capturing word HyperLex.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3274489