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.
The literature on semantic parsing has focused on various types of semantic formalisms. The @math -calculus expressions @cite_19 have been popular and widely used in semantic parsing tasks over recent years @cite_32 @cite_25 @cite_18 @cite_16 @cite_41 @cite_24 . Dependency-based compositional semantics (DCS) Unlike ours, their work captures dependencies between semantic units but not natural language words. was introduced by , whose extension, @math -DCS, was later proposed by . Various models @cite_0 @cite_31 @cite_35 on semantic parsing with the @math -DCS formalism were proposed. In this work, we focus on the tree-structured semantic formalism which has been examined by various research efforts @cite_17 @cite_13 @cite_3 @cite_23 @cite_48 @cite_15 @cite_36 .
@cite_35 @cite_18 @cite_15 @cite_41 @cite_48 @cite_36 @cite_32 @cite_3 @cite_24 @cite_19 @cite_0 @cite_23 @cite_31 @cite_16 @cite_13 @cite_25 @cite_17
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Suppose we want to build a system that answers a natural language question by representing its semantics as a logical form and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to learn a semantic parser from question-answer pairs instead, where the logical form is modeled as a latent variable. Motivated by this challenging learning problem, we develop a new semantic formalism, dependency-based compositional semantics (DCS), which has favorable linguistic, statistical, and computational properties. We define a log-linear distribution over DCS logical forms and estimate the parameters using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, our system outperforms all existing state-of-the-art systems, despite using no annotated logical forms. Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This mapping allows them to effectively leverage the information contained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting---these semantic parsers can only assign meaning to language that falls within the KB's manually-produced schema. Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base. However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models. We show how to combine the disparate representations used by these two approaches, presenting for the first time a semantic parser that (1) produces compositional, executable representations of language, (2) can successfully leverage the information contained in both a formal KB and a large corpus, and (3) is not limited to the schema of the underlying KB. We demonstrate significantly improved performance over state-of-the-art baselines on an open-domain natural language question answering task. 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 . We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches---one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at this https URL