Portmanteaus are a word formation phenomenon where two words are combined to form a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
generate new words to describe a product given its category and properties. However, their method is limited to hand-crafted rules as compared to our data driven approach. Also, their focus is on brand names. have proposed an approach to recommend brand names based on brand product description. However, they consider only a limited number of features like memorability and readability. devise an approach to generate portmanteaus, which requires user-defined weights for attributes like . Generating a portmanteau from two root words can be viewed as a S2S problem. Recently, neural approaches have been used for S2S problems @cite_2 such as MT. and have shown that character-level neural sequence models work as well as word-level ones for language modelling and MT. propose S2S models for multi-source MT, which have multi-sequence inputs, similar to our case.
@cite_2
2550147980
We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.