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Practical Semantic Parsing for Spoken Language Understanding

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Practical Semantic Parsing for Spoken Language Understanding Practical Semantic Parsing for Spoken Language Understanding Marco Damonte∗ Rahul Goel Tagyoung Chung University of Edinburgh Amazon Alexa AI [email protected] fgoerahul,[email protected] Abstract the intent’s slots. This architecture is popular due to its simplicity and robustness. On the other hand, Executable semantic parsing is the task of con- verting natural language utterances into logi- Q&A, which need systems to produce more com- cal forms that can be directly used as queries plex structures such as trees and graphs, requires a to get a response. We build a transfer learn- more comprehensive understanding of human lan- ing framework for executable semantic pars- guage. ing. We show that the framework is effec- One possible system that can handle such a task tive for Question Answering (Q&A) as well as is an executable semantic parser (Liang, 2013; for Spoken Language Understanding (SLU). Kate et al., 2005). Given a user utterance, an exe- We further investigate the case where a parser on a new domain can be learned by exploit- cutable semantic parser can generate tree or graph ing data on other domains, either via multi- structures that represent logical forms that can be task learning between the target domain and used to query a knowledge base or database. In an auxiliary domain or via pre-training on the this work, we propose executable semantic pars- auxiliary domain and fine-tuning on the target ing as a common framework for both uses cases domain. With either flavor of transfer learn- by framing SLU as executable semantic parsing ing, we are able to improve performance on that unifies the two use cases. For Q&A, the in- most domains; we experiment with public data sets such as Overnight and NLmaps as well as put utterances are parsed into logical forms that with commercial SLU data. The experiments represent the machine-readable representation of carried out on data sets that are different in the question, while in SLU, they represent the nature show how executable semantic parsing machine-readable representation of the user intent can unify different areas of NLP such as Q&A and slots. One added advantage of using parsing and SLU. for SLU is the ability to handle more complex lin- 1 Introduction guistic phenomena such as coordinated intents that traditional SLU systems struggle to handle (Agar- Due to recent advances in speech recognition and wal et al., 2018). Our parsing model is an exten- language understanding, conversational interfaces sion of the neural transition-based parser of Cheng such as Alexa, Cortana, and Siri are becoming et al.(2017). more common. They currently have two large uses A major issue with semantic parsing is the avail- cases. First, a user can use them to complete a ability of the annotated logical forms to train the specific task, such as playing music. Second, a parsers, which are expensive to obtain. A solution user can use them to ask questions where the ques- is to rely more on distant supervisions such as by tions are answered by querying knowledge graph using question–answer pairs (Clarke et al., 2010; or database back-end. Typically, under a common Liang et al., 2013). Alternatively, it is possible interface, there exist two disparate systems that to exploit annotated logical forms from a differ- can handle each use cases. The system underlying ent domain or related data set. In this paper, we the first use case is known as a spoken language focus on the scenario where data sets for several understanding (SLU) system. Typical commercial domains exist but only very little data for a new SLU systems rely on predicting a coarse user in- one is available and apply transfer learning tech- tent and then tagging each word in the utterance to niques to it. A common way to implement trans- ∗Work conducted while interning at Amazon Alexa AI. fer learning is by first pre-training the model on a 16 Proceedings of NAACL-HLT 2019, pages 16–23 Minneapolis, Minnesota, June 2 - June 7, 2019. c 2019 Association for Computational Linguistics domain on which a large data set is available and and they have been also applied to semantic pars- subsequently fine-tuning the model on the target ing tasks (Wang et al., 2015a; Cheng et al., 2017). domain (Thrun, 1996; Zoph et al., 2016). We also In syntactic parsing, a transition system is usu- consider a multi-task learning (MTL) approach. ally defined as a quadruple: T = fS; A; I; Eg, MTL refers to machine learning models that im- where S is a set of states, A is a set of actions, prove generalization by training on more than one I is the initial state, and E is a set of end states. task. MTL has been used for a number of NLP A state is composed of a buffer, a stack, and a problems such as tagging (Collobert and Weston, set of arcs: S = (β; σ; A). In the initial state, 2008), syntactic parsing (Luong et al., 2015), ma- the buffer contains all the words in the input sen- chine translation (Dong et al., 2015; Luong et al., tence while the stack and the set of subtrees are 2015) and semantic parsing (Fan et al., 2017). See empty: S0 = (w0j ::: jwN ; ;; ;). Terminal states Caruana(1997) and Ruder(2017) for an overview have empty stack and buffer: ST = (;; ;;A). of MTL. During parsing, the stack stores words that have A good Q&A data set for our domain adap- been removed from the buffer but have not been tation scenario is the Overnight data set (Wang fully processed yet. Actions can be performed et al., 2015b), which contains sentences annotated to advance the transition system’s state: they can with Lambda Dependency-Based Compositional either consume words in the buffer and move Semantics (Lambda DCS; Liang 2013) for eight them to the stack (SHIFT) or combine words in different domains. However, it includes only a the stack to create new arcs (LEFT-ARC and few hundred sentences for each domain, and its RIGHT-ARC, depending on the direction of the vocabularies are relatively small. We also ex- arc)1. Words in the buffer are processed left-to- periment with a larger semantic parsing data set right until an end state is reached, at which point (NLmaps; Lawrence and Riezler 2016). For SLU, the set of arcs will contain the full output tree. we work with data from a commercial conversa- The parser needs to be able to predict the next tional assistant that has a much larger vocabulary action based on its current state. Traditionally, su- size. One common issue in parsing is how to pervised techniques are used to learn such clas- deal with rare or unknown words, which is usu- sifiers, using a parallel corpus of sentences and ally addressed by either delexicalization or by im- their output trees. Trees can be converted to states plementing a copy mechanism (Gulcehre et al., and actions using an oracle system. For a detailed 2016). We show clear differences in the outcome explanation of transition-based parsing, see Nivre of these and other techniques when applied to data (2003) and Nivre(2008). sets of varying sizes. Our contributions are as fol- lows: 2.1 Neural Transition-based Parser with Stack-LSTMs • We propose a common semantic parsing In this paper, we consider the neural executable framework for Q&A and SLU and demon- semantic parser of Cheng et al.(2017), which fol- strate its broad applicability and effective- lows the transition-based parsing paradigm. Its ness. transition system differs from traditional systems • We report parsing baselines for Overnight for as the words are not consumed from the buffer be- which exact match parsing scores have not cause in executable semantic parsing, there are no been yet published. strict alignments between words in the input and • We show that SLU greatly benefits from a nodes in the tree. The neural architecture encodes copy mechanism, which is also beneficial for the buffer using a Bi-LSTM (Graves, 2012) and NLmaps but not Overnight. the stack as a Stack-LSTM (Dyer et al., 2015), a recurrent network that allows for push and pop • We investigate the use of transfer learning operations. Additionally, the previous actions are and show that it can facilitate parsing on low- also represented with an LSTM. The output of resource domains. these networks is fed into feed-forward layers and 2 Transition-based Parser softmax layers are used to predict the next action 1There are multiple different transition systems. The ex- Transition-based parsers are widely used for de- ample we describe here is that of arc-standard system (Nivre, pendency parsing (Nivre, 2008; Dyer et al., 2015) 2004) for projective dependency parsing. 17 given the current state. The possible actions are NT REDUCE, which pops an item from the stack, nt0 ::: ntn TER, which creates a terminal node (i.e., a leaf in the tree), and NT, which creates a non-terminal TER COPY node. When the next action is either TER or NT, t0 ::: tn x0 ::: xn additional softmax layers predict the output token to be generated. Since the buffer does not change while parsing, an attention mechanism is used to TER RED NT focus on specific words given the current state of the parser. FEED-FORWARD We extend the model of Cheng et al.(2017) LAYERS by adding character-level embeddings and a copy mechanism. When using only word embeddings, ATTENTION out-of-vocabulary words are usually mapped to one embedding vector and do not exploit morpho- ::: ::: ::: logical features.
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