Zhang, L., & Moldovan, D. Classification of Semantic Relations between Pairs of Nominals Using Transfer Learning.
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Abstract
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Re-train these 8 popular neural network sentence encoding structures and use them as the starting points on relation classification task, and show that their models could achieve comparable results to the state-of-the-art relation classification systems. The representation of semantic meaning of sentences using neural network has recently gained popularity, due to the fact that there is no need to specifically extract lexical syntactic and semantic features. A major problem with this approach is that it requires large human annotated corpora. In order to reduce human annotation effort, in recent years, researchers made several attempts to find universal sentence representation methods, aiming to obtain general-purpose sentence embeddings that could be widely adopted to a wide range of NLP tasks without training directly from the specific datasets. In-ferSent, a supervised universal sentence representation model proposed by Facebook research, implements 8 popular neural network sentence encoding structures trained on natural language inference datasets, and apply to 12 different NLP tasks. However, the relation classification task was not one of these. In this paper, we re-train these 8 sentence encoding structures and use them as the starting points on relation classification task. Experiments using SemEval-2010 datasets show that our models could achieve comparable results to the state-of-the-art relation classification systems.
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