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Unsupervised Natural Language Inference Using PHL Triplet Generation [article]

Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, Chitta Baral
2022 arXiv   pre-print
Transformer-based models achieve impressive performance on numerous Natural Language Inference (NLI) benchmarks when trained on respective training datasets.  ...  As a solution, we propose a procedural data generation approach that leverages a set of sentence transformations to collect PHL (Premise, Hypothesis, Label) triplets for training NLI models, bypassing  ...  Introduction Natural Language Inference (NLI) is the task of determining whether a "hypothesis" is true (Entailment), false (Contradiction), or undetermined (Neutral) given a "premise".  ... 
arXiv:2110.08438v2 fatcat:foxtf2abdrcfzkhtwnhdlxuzkm

An Inference Approach To Question Answering Over Knowledge Graphs [article]

Aayushee Gupta, K.M. Annervaz, Ambedkar Dukkipati, Shubhashis Sengupta
2021 arXiv   pre-print
In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs.  ...  Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem.  ...  An illustrative example of the generated PHL triplet is given in Table 1 . Premise-Hypothesis-Label(PHL) triplet data was then further split to test and train the inference models.  ... 
arXiv:2112.11070v1 fatcat:i2bb7zl2tvadpdlp25tmjc5kby

A Network-based Approach to Breast Cancer Systems Medicine [article]

ELEONORA LUSITO
2015
(e.g. microarray gene expression profiling, next-generation sequencing).  ...  of breast cancer have led to a significant decrease in the mortality rate, the identification of an optimal therapeutic strategy for each patient remains a difficult task because of the heterogeneous nature  ...  We applied the Cohen test on each triplet of networks inferred from each CM-gene. In total, we performed comparisons for 1,516 triplets.  ... 
doi:10.13130/lusito-eleonora_phd2015-03-18 fatcat:dyawnspuczfmpkuniianpxgmry