Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection

By Katharina Kann, Hinrich Schütze. 2016

94 citations recorded by the source corpus.

Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag. We propose a new way of modeling this task with neural encoder-decoder models. Our approach reduces the amount of required training data for this architecture and achieves state-of-the-art results, making encoder-decoder models applicable to morphological reinflection even for low-resource languages. We further present a new automatic correction method for the outputs based on edit trees.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Neural Generation for Czech: Data and Baselines (2019, background): “, 2016, 2017), or morphological inflection (e.g. Faruqui et al., 2016; Kann and Schütze, 2016).”
Morphological Inflection Generation with Hard Monotonic Attention (2016, methodology): “We compare our model to two soft attention baselines on this dataset: MED (Kann and Sch¨utze, 2016b), which was the best participating system in the shared task, and our implementation of the global (soft) attention model presented by Luong et al. (2015).”
Gender-Aware Reinflection using Linguistically Enhanced Neural Models (2020, unknown): “Sequence-to-sequence models have achieved signiﬁcant results in grammatical error correction (GEC) (Chollampatt and Ng, 2018; Junczys-Dowmunt et al., 2018; Grundkiewicz et al., 2019) and morphological reinﬂection tasks (Faruqui et al., 2016; Kann and Schütze, 2016; Aharoni and Goldberg, 2017).”