Multilingual Semantic Parsing : Parsing Multiple Languages into Semantic Representations

Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 1291–1301, Dublin, Ireland, August 23-29 2014. Multilingual Semantic Parsing : Parsing Multiple Languages into Semantic Representations Zhanming Jie University of Electronic Science & Technology of China allanmcgrady@gmail.com Wei Lu Information Systems Technology and Design Singapore University of Technology and Design luwei@sutd.edu.sg Abstract We consider multilingual semantic parsing – the task of simultaneously parsing semantically equivalent sentences from multiple different languages into their corresponding formal semantic representations. Our model is built on top of the hybrid tree semantic parsing framework, where natural language sentences and their corresponding semantics are assumed to be generated jointly from an underlying generative process. We first introduce a variant of the joint generative pro- cess, which essentially gives us a new semantic parsing model within the framework. Based on the different models that can be developed within the framework, we then investigate several ap- proaches for performing the multilingual semantic parsing task. We present our evaluations on a standard dataset annotated with sentences in multiple languages coming from different language families. 1 Introduction Semantic parsing, the task of parsing natural language sentences into their formal semantic representa- tions (Mooney, 2007) is one of the most important tasks in the field of natural language processing and artificial intelligence. This area of research recently has received a significant amount of attention (Zettle- moyer and Collins, 2005; Kate and Mooney, 2006; Wong and Mooney, 2006; Lu et al., 2008; Jones et al., 2012b). Consider these example sentence-semantics pairs: English: Which states have points that are higher than the highest point in Texas ? Semantics: answer(state(loc1(place(higher2(highest(place(loc2(stateid(′TX′))))))))) English: What rivers do not run through Tennessee ? Semantics: answer(exclude(river(all), traverse2(stateid(′TN ′)))) In the typical setting, the semantic parser learns from a collection of such sentence-semantics pairs a model that can parse novel input sentences into their respective semantic representations. Such semantic representations can then be used to interact with certain downstream components to perform interesting tasks. For example, retrieving of answers from an underlying database, or performing certain actions based on the generated executable semantic instructions. Note that in the training data, although complete sentence-semantics pairs are given, specific word- level semantic information is not explicitly provided. The model therefore needs to automatically learn such latent mappings between natural language words/phrases and semantic units. One natural assumption is that the semantics exhibit certain restricted structures, such as the recursive tree structures. Under such an assumption, one can convert the second semantics appeared above as the tree structure illustrated in Figure 1. More details about such tree structured representations will be given in Section 2.1. Currently, researchers only focused on the semantic parsing task under a single language setting where the input is a sentence from one particular language. However, natural language is highly ambiguous, and identifying the correct semantics associated with words with limited background information is a This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by …

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