We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations. Unlike previous state-of-the-art models, the semantic information is interpreted as the latent dependency between the natural language words in our joint representation. Such dependency information can capture the interactions between the semantics and natural language words. We integrate a neural component into our model and propose an efficient dynamic-programming algorithm to perform tractable inference. Through extensive experiments on the standard multilingual GeoQuery dataset with eight languages, we demonstrate that our proposed approach is able to achieve state-of-the-art performance across several languages. Analysis also justifies the effectiveness of using our new dependency-based representation.
@cite_15: We propose a novel model for parsing natural language sentences into their formal semantic representations. The model is able to perform integrated lexicon acquisition and semantic parsing, mapping each atomic element in a complete semantic representation to a contiguous word sequence in the input sentence in a recursive manner, where certain overlappings amongst such word sequences are allowed. It defines distributions over the novel relaxed hybrid tree structures which jointly represent both sentences and semantics. Such structures allow tractable dynamic programming algorithms to be developed for efficient learning and decoding. Trained under a discriminative setting, our model is able to incorporate a rich set of features where certain unbounded long-distance dependencies can be captured in a principled manner. We demonstrate through experiments that by exploiting a large collection of simple features, our model is shown to be competitive to previous works and achieves state-of-theart performance on standard benchmark data across four different languages. The system and code can be downloaded from http: statnlp.org research sp .
(left) @cite_15 and our dependency-based hybrid tree (right) as well as the flat representation (bottom right) of the example in Figure .