A Neural Approach for Detecting Morphological Analogies
This addresses a specific challenge in NLP for tasks like reasoning and classification, but it is incremental as it applies an existing deep learning paradigm to a known problem.
The paper tackles the problem of detecting morphological analogies, such as reinflexion or conjugation, by proposing a deep learning approach, and shows it is competitive with state-of-the-art symbolic methods while exploring cross-language transferability.
Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.