Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
This work addresses a challenging semantic classification task in natural language processing, but it is incremental as it applies existing transfer and multi-task learning techniques to a specific domain.
The paper tackled the problem of semantic interpretation of noun-noun compounds by evaluating transfer and multi-task learning, showing that these methods improve generalization over skewed relation distributions and boost accuracy and F1 scores on less frequent relations.
In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.