Neural Sequence Models

AbstractWe present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. …...

AbstractGrammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based …...

AbstractHow do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic comparisons of RNNs’ …...

AbstractFew works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is …...

AbstractHistorical linguists have identified regularities in the process of historic sound change. The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages. Can this …...

Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input. These variety …...

AbstractBeam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does …...

Neural sequence models exhibit limited com- positional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynam- ically recombining structured expressions in a recursive manner. However, most …...

We computationally model the processes of word borrowing from a donor word to an in- corporated word, and vice versa, by answer- ing two questions: (1) what does a word …...