DOCSLIB.ORG

A Recursive Semantic Parsing Framework for Text-To-SQL Task

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Recursive Semantic Parsing Framework for Text-To-SQL Task Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task Yu Zeng1∗ , Yan Gao2 , Jiaqi Guo3∗ , Bei Chen2 , Qian Liu4∗ , Jian-Guang Lou2 , Fei Tengy1 and Dongmei Zhang2 1Southwest Jiaotong University, Chengdu, China 2Microsoft Research Asia, Beijing, China 3Xi’an Jiaotong University, Xi’an, China 4Beihang University, Beijing, China [email protected], fyan.gao, beichen, jlou, [email protected], [email protected], [email protected], [email protected] list all song names by singers age above the Abstract Utterance: average singer age. Neural semantic parsers usually fail to parse SELECT song_name FROM singers SQL Query: long and complicated utterances into nested SQL WHERE age > (SELECT avg(age) FROM singers) queries, due to the large search space. In this pa- per, we propose a novel recursive semantic pars- Figure 1: An example of the component mapping relationship be- ing framework called RECPARSER to generate the tween the nested SQL query and its corresponding utterance. nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation prob- lem into several progressive non-nested SQL query In this paper, we focus on the problem of composing nested generation problems. Furthermore, we propose SQL queries in Text-to-SQL task. Firstly, as we know the a novel Question Decomposer module to explic- nested SQL query representation is formalized as a kind of itly encourage RECPARSER to focus on different recursive structure, thus a nested SQL query generation prob- components of an utterance when predicting SQL lem can be decomposed into several progressive non-nested queries of different layers. Experiments on the Spi- SQL query generation problems by nature. Furthermore, we der dataset show that our approach is more effec- found that there is a strong component mapping relation- tive compared to the previous works at predicting ship between the nested SQL query and its corresponding the nested SQL queries. In addition, we achieve an utterance. Figure 1 shows an example of the component overall accuracy that is comparable with state-of- mapping relationship between the nested SQL query and its the-art approaches. corresponding utterance. We see that, for this nested SQL query, the outside layer “SELECT song name FROM singers WHERE age >” and the inside layer “SELECT avg(age) 1 Introduction FROM singers” correspond to different utterance components “list all song names by singers age above” and “the average Text-to-SQL is one of the most important sub-tasks of se- singer age” respectively. mantic parsing in natural language processing (NLP). It maps natural language utterances to corresponding SQL queries. Inspired by these observations, we propose a recursive se- By helping non-experts to interact with ever increasing mantic parsing framework called RECPARSER to generate databases, the task has many important potential applica- the nested SQL query layer-by-layer from outside to inside. tions in real life, and thus receives a great deal of interest Furthermore, we propose a novel Question Decomposer mod- from both industry and academia [Li and Jagadish, 2016; ule to explicitly encourage RECPARSER to focus on different Zhong et al., 2017; Affolter et al., 2019]. parts of an utterance when predicting SQL queries in differ- Composing nested SQL queries is a challenging problem in ent layers. RECPARSER consists of four modules: Initial Text-to-SQL. Several works [Guo et al., 2019; Zhang et al., Encoder, Iterative Encoder, Question Decomposer and SQL 2019b; Bogin et al., 2019; Yu et al., 2018a] have attempted Generator. In the initialization stage, we encode the user to deal with this problem on the recently proposed dataset utterance and the database schema with the Initial Encoder. Spider [Yu et al., 2018b], which contains nested SQL queries Then, by recursively calling the Question Decomposer, the over different databases with multiple tables. However, due Iterative Encoder and the SQL Generator modules, we gener- to the large search space, existing neural semantic parsers do ate the nested SQL query layer-by-layer. Finally, we compose not perform well on composing nested SQL queries [Zhang SQL queries of different layers into the whole SQL query et al., 2019a; Affolter et al., 2019]. when encountering a recursive termination condition. Con- cretely, in each recursion round, the Question Decomposer ∗Work done during an internship at Microsoft Research. updates the utterance representation with a soft-attention- yCorresponding Author mask mechanism and a recursive-attention-divergency loss 3644 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) proposed by us for the next recursion; the Iterative Encoder Recursive Mechanism in Semantic Parsing. Recursive contextualizes the schema representation with the updated ut- mechanism has been successfully used in complicated se- terance representation; the SQL Generator takes the contextu- mantic parsing tasks [Andreas et al., 2016; Rabinovich et alized schema representation as input and generates the non- al., 2017]. Recently, several works [Yu et al., 2018a; nested SQL query of the current layer. Our SQL Generator is Lee, 2019] also employ the recursive mechanism to gener- a simple yet effective multi-task classification model. ate nested SQL queries in Text-to-SQL task. SyntaxSQL- We argue that RECPARSER has two advantages. Firstly, Net [Yu et al., 2018a] employs a SQL specific syntax tree- in this divide-and-conquer faction, we simplify the difficulty based decoder that calls a collection of recursive modules of the nested query generation problem. Our SQL Genera- for decoding. RCSQL [Lee, 2019] proposes a SQL clause- tor only learns to generate non-nested SQL queries instead of wise decoding architecture. It recursively calls different SQL the whole nested SQL query, thus largely reducing the search clause decoders to predict nested SQL queries. Compared space and alleviating training difficulties. Secondly, through with their approaches, we recursively call both encoder mod- updating the utterance representation recursively with the ule and SQL generator module for each recursion, while they soft-attention-mask mechanism and the recursive-attention- only call a set of SQL generator modules. In addition, we divergency loss, RECPARSER can focus on different parts of propose a novel Question Decomposer module to capture the an utterance when predicting different SQL query layers, thus mapping relationship between different SQL query layers and minimizing the interference of irrelevant parts. their corresponding components in utterance. On the Spider benchmark [Yu et al., 2018b], RECPARSER achieves a state-of-the-art 39.3% accuracy on the nested SQL Decomposing Complicated Question. Decomposing query and a comparable 54.3% accuracy on the overall SQL complicated question is widely used in many semantic query. When augmented with BERT [Devlin et al., 2018], parsing works [Iyyer et al., 2016; Talmor and Berant, 2018; RECPARSER reaches up to a state-of-the-art 46.8% accu- Zhang et al., 2019a]. They utilize the decompositionality racy on the nested SQL query and a state-of-the-art 63.1% of complicated questions to help question understanding. accuracy on the overall SQL query. Our contributions are Inspired by them, we use the soft-attention-mask mechanism summarized as follows. and the recursive-attention-divergency loss to model the de- • We propose a recursive semantic parsing framework composing process of the utterance and update the utterance called RECPARSER to decompose a complicated nested representation for next recursion round. In this way, we don’t SQL query generation problem into several progressive need to make hard decisions and it could be learned in an non-nested SQL query generation problems. end-to-end fashion without extra labeling work. • We propose a novel Question Decomposer module Multi-Task Learning in Text-to-SQL Task. In multi-task with a soft-attention-mask mechanism and a recursive- learning, the Text-to-SQL task is decomposed into several attention-divergency loss to explicitly encourage REC- sub-tasks, each predicting a part of the final SQL program. PARSER to focus on different parts of the utterance Compared with sequence-to-sequence-style models, multi- when predicting SQL queries in different layers. task learning does not require the SQL queries to be serial- • Our approach is more effective compared to previous ized and thus avoid the “ordering issue” [Xu et al., 2018]. works at predicting nested SQL queries. In addition, Existing state-of-the-art multi-task learning methods [Hwang we achieve an overall accuracy that is comparable with et al., 2019; He et al., 2019] have already surpassed human state-of-the-art approaches. performance on WikiSQL [Zhong et al., 2017] dataset. How- ever, existing methods are limited to the specific SQL sketch 2 Related Works of WikiSQL, which only supports very simple queries. In this Composing Nested SQL Query. The problem of compos- work, we propose a simple yet effective multi-task classifica- ing nested SQL queries has been studied for decades [An- tion model to generate arbitrary non-nested SQL query as the droutsopoulos et al., 1995]. Most of the early proposed sys- SQL Generator module in our framework. tems are rule-based [Popescu et al., 2003; Li and Jagadish, 2014]. Recently, with the development of advanced neural approaches and the release of the complex and cross-domain 3 Methodology Text-to-SQL dataset Spider [Yu et al., 2018b], several neu- ral semantic parsers [Guo et al., 2019; Zhang et al., 2019b; In this section, we will describe our RECPARSER framework Bogin et al., 2019; Lee, 2019] have attempted to generate in detail. As described in Figure 2, RECPARSER consists of the nested SQL queries with a complicated grammar-based four modules: Initial Encoder, Iterative Encoder, Question decoder. However, applying grammar-based decoders to gen- Decomposer and SQL Generator.
Load more

Recommended publications
  • Semantic Parsing for Single-Relation Question Answering
    Semantic Parsing for Single-Relation Question Answering Wen-tau Yih Xiaodong He Christopher Meek Microsoft Research Redmond, WA 98052, USA scottyih,xiaohe,meek @microsoft.com { } Abstract the same question. That is to say that the problem of mapping from a question to a particular relation We develop a semantic parsing framework and entity in the KB is non-trivial. based on semantic similarity for open do- In this paper, we propose a semantic parsing main question answering (QA). We focus framework tailored to single-relation questions. on single-relation questions and decom- At the core of our approach is a novel semantic pose each question into an entity men- similarity model using convolutional neural net- tion and a relation pattern. Using convo- works. Leveraging the question paraphrase data lutional neural network models, we mea- mined from the WikiAnswers corpus by Fader et sure the similarity of entity mentions with al. (2013), we train two semantic similarity mod- entities in the knowledge base (KB) and els: one links a mention from the question to an the similarity of relation patterns and re- entity in the KB and the other maps a relation pat- lations in the KB. We score relational tern to a relation. The answer to the question can triples in the KB using these measures thus be derived by finding the relation–entity triple and select the top scoring relational triple r(e1, e2) in the KB and returning the entity not to answer the question. When evaluated mentioned in the question. By using a general se- on an open-domain QA task, our method mantic similarity model to match patterns and re- achieves higher precision across different lations, as well as mentions and entities, our sys- recall points compared to the previous ap- tem outperforms the existing rule learning system, proach, and can improve F1 by 7 points.
    [Show full text]
  • Semantic Parsing with Semi-Supervised Sequential Autoencoders
    Semantic Parsing with Semi-Supervised Sequential Autoencoders Toma´sˇ Kociskˇ y´†‡ Gabor´ Melis† Edward Grefenstette† Chris Dyer† Wang Ling† Phil Blunsom†‡ Karl Moritz Hermann† †Google DeepMind ‡University of Oxford tkocisky,melisgl,etg,cdyer,lingwang,pblunsom,kmh @google.com { } Abstract This is common in situations where we encode nat- ural language into a logical form governed by some We present a novel semi-supervised approach grammar or database. for sequence transduction and apply it to se- mantic parsing. The unsupervised component While such an autoencoder could in principle is based on a generative model in which latent be constructed by stacking two sequence transduc- sentences generate the unpaired logical forms. ers, modelling the latent variable as a series of dis- We apply this method to a number of semantic crete symbols drawn from multinomial distributions parsing tasks focusing on domains with lim- ited access to labelled training data and ex- creates serious computational challenges, as it re- tend those datasets with synthetically gener- quires marginalising over the space of latent se- ated logical forms. quences Σx∗. To avoid this intractable marginalisa- tion, we introduce a novel differentiable alternative for draws from a softmax which can be used with 1 Introduction the reparametrisation trick of Kingma and Welling (2014). Rather than drawing a discrete symbol in Neural approaches, in particular attention-based Σx from a softmax, we draw a distribution over sym- sequence-to-sequence models, have shown great bols from a logistic-normal distribution at each time promise and obtained state-of-the-art performance step. These serve as continuous relaxations of dis- for sequence transduction tasks including machine crete samples, providing a differentiable estimator translation (Bahdanau et al., 2015), syntactic con- of the expected reconstruction log likelihood.
    [Show full text]
  • SQL Generation from Natural Language: a Sequence-To- Sequence Model Powered by the Transformers Architecture and Association Rules
    Journal of Computer Science Original Research Paper SQL Generation from Natural Language: A Sequence-to- Sequence Model Powered by the Transformers Architecture and Association Rules 1,2Youssef Mellah, 1Abdelkader Rhouati, 1El Hassane Ettifouri, 2Toumi Bouchentouf and 2Mohammed Ghaouth Belkasmi 1NovyLab Research, Novelis, Paris, France 2Mohammed First University Oujda, National School of Applied Sciences, LaRSA/SmartICT Lab, Oujda, Morocco Article history Abstract: Using Natural Language (NL) to interacting with relational Received: 12-03-2021 databases allows users from any background to easily query and analyze Revised: 21-04-2021 large amounts of data. This requires a system that understands user Accepted: 28-04-2021 questions and automatically converts them into structured query language such as SQL. The best performing Text-to-SQL systems use supervised Corresponding Author: Youssef Mellah learning (usually formulated as a classification problem) by approaching NovyLab Research, Novelis, this task as a sketch-based slot-filling problem, or by first converting Paris, France questions into an Intermediate Logical Form (ILF) then convert it to the And corresponding SQL query. However, non-supervised modeling that LARSA Laboratory, ENSAO, directly converts questions to SQL queries has proven more difficult. In Mohammed First University, this sense, we propose an approach to directly translate NL questions into Oujda, Morocco SQL statements. In this study, we present a Sequence-to-Sequence Email: [email protected] (Seq2Seq) parsing model for the NL to SQL task, powered by the Transformers Architecture exploring the two Language Models (LM): Text-To-Text Transfer Transformer (T5) and the Multilingual pre-trained Text-To-Text Transformer (mT5). Besides, we adopt the transformation- based learning algorithm to update the aggregation predictions based on association rules.
    [Show full text]
  • Semantic Parsing with Neural Hybrid Trees
    Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Semantic Parsing with Neural Hybrid Trees Raymond Hendy Susanto, Wei Lu Singapore University of Technology and Design 8 Somapah Road, Singapore 487372 {raymond susanto, luwei}@sutd.edu.sg Abstract QUERY : answer(STATE) We propose a neural graphical model for parsing natural lan- STATE : exclude(STATE, STATE) guage sentences into their logical representations. The graph- ical model is based on hybrid tree structures that jointly rep- : state( ) : next to( ) resent both sentences and semantics. Learning and decoding STATE all STATE STATE are done using efficient dynamic programming algorithms. The model is trained under a discriminative setting, which STATE : stateid(STATENAME) allows us to incorporate a rich set of features. Hybrid tree structures have shown to achieve state-of-the-art results on STATENAME :(tx ) standard semantic parsing datasets. In this work, we propose a novel model that incorporates a rich, nonlinear featurization Which states do not border Texas ? by a feedforward neural network. The error signals are com- puted with respect to the conditional random fields (CRFs) objective using an inside-outside algorithm, which are then Figure 1: An example tree-structured semantic representa- backpropagated to the neural network. We demonstrate that tion and its corresponding natural language sentence. by combining the strengths of the exact global inference in the hybrid tree models and the power of neural networks to extract high level features, our model is able to achieve new explicit labelings between words and meaning representa- state-of-the-art results on standard benchmark datasets across tion tokens are not available in the training data.
    [Show full text]
  • Learning Logical Representations from Natural Languages with Weak Supervision and Back-Translation
    Learning Logical Representations from Natural Languages with Weak Supervision and Back-Translation Kaylin Hagopiany∗ Qing Wangy Georgia Institute of Technology IBM Research Atlanta, GA, USA Yorktown Heights, NY, USA [email protected] [email protected] Tengfei Ma Yupeng Gao Lingfei Wu IBM Research AI IBM Research AI IBM Research AI Yorktown Heights, NY, USA Yorktown Heights, NY, USA Yorktown Heights, NY, USA [email protected] [email protected] [email protected] Abstract Semantic parsing with strong supervision requires a large parallel corpus for train- ing, whose production may be impractical due to the excessive human labour needed for annotation. Recently, weakly supervised approaches based on reinforce- ment learning have been studied, wherein only denotations, but not the logical forms, are available. However, reinforcement learning approaches based on, e.g., policy gradient training, suffer reward sparsity and progress slowly. In this work, we explore enriching the policy gradient framework with a back-translation mecha- nism that boosts the reward signal. In this vein, we jointly optimize the expected reward of obtaining the correct denotation and the likelihood of reconstructing the natural language utterance. Initial experimental results suggest the proposed method slightly outperforms a state-of-the-art semantic parser trained with policy gradient. Introduction Semantic parsing is the task of converting natural language utterances to logical representations that a machine can understand and execute as problem solving steps. There has recently been a surge of interest in developing machine learning methods for semantic parsing, owing to the creation of large- scale text corpora. There has also been a proliferation of logical representations used in this setting, including logical forms [28], domain specific languages [26], python code [24], SQL queries [31], and bash commands [20].
    [Show full text]
  • A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
    Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing Subendhu Rongali∗ Luca Soldaini University of Massachusetts Amherst Amazon Alexa Search Amherst, MA, USA Manhattan Beach, CA, USA [email protected] [email protected] Emilio Monti Wael Hamza Amazon Alexa Amazon Alexa AI Cambridge, UK New York, NY, USA [email protected] [email protected] ABSTRACT ArtistName Virtual assistants such as Amazon Alexa, Apple Siri, and Google Play the song don't stop believin by Journey Assistant often rely on a semantic parsing component to understand PlaySongIntent SongName which action(s) to execute for an utterance spoken by its users. Traditionally, rule-based or statistical slot-filling systems have been Intent: PlaySongIntent used to parse “simple” queries; that is, queries that contain a single Slots: SongName(don't stop believin), action and can be decomposed into a set of non-overlapping entities. ArtistName(Journey) More recently, shift-reduce parsers have been proposed to process more complex utterances. These methods, while powerful, impose specific limitations on the type of queries that can be parsed; namely, Figure 1: Semantic parsing of a “simple” query. Simple they require a query to be representable as a parse tree. queries define single action (intent) and can be decomposed In this work, we propose a unified architecture based on Se- into a set of non-overlapping entities (slots). quence to Sequence models and Pointer Generator Network to handle both simple and complex queries. Unlike other works, our 1 INTRODUCTION approach does not impose any restriction on the semantic parse Adoption of intelligent voice assistants such as Amazon Alexa, schema.
    [Show full text]
  • Neural Semantic Parsing Graham Neubig
    CS11-747 Neural Networks for NLP Neural Semantic Parsing Graham Neubig Site https://phontron.com/class/nn4nlp2018/ Tree Structures of Syntax • Dependency: focus on relations between words ROOT I saw a girl with a telescope • Phrase structure: focus on the structure of the sentence S VP PP NP NP PRP VBD DT NN IN DT NN I saw a girl with a telescope Representations of Semantics • Syntax only gives us the sentence structure • We would like to know what the sentence really means • Specifically, in an grounded and operationalizable way, so a machine can • Answer questions • Follow commands • etc. Meaning Representations • Special-purpose representations: designed for a specific task • General-purpose representations: designed to be useful for just about anything • Shallow representations: designed to only capture part of the meaning (for expediency) Parsing to Special-purpose Meaning Representations Example Special-purpose Representations • A database query language for sentence understanding • A robot command and control language • Source code in a language such as Python (?) Example Query Tasks • Geoquery: Parsing to Prolog queries over small database (Zelle and Mooney 1996) • Free917: Parsing to Freebase query language (Cai and Yates 2013) • Many others: WebQuestions, WikiTables, etc. Example Command and Control Tasks • Robocup: Robot command and control (Wong and Mooney 2006) • If this then that: Commands to smartphone interfaces (Quirk et al. 2015) Example Code Generation Tasks • Hearthstone cards (Ling et al. 2015) • Django commands (Oda
    [Show full text]
  • 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.
    [Show full text]
  • Arxiv:2009.14116V1 [Cs.CL] 29 Sep 2020
    A Survey on Semantic Parsing from the Perspective of Compositionality Pawan Kumar Srikanta Bedathur Indian Institute of Technology Delhi, Indian Institute of Technology Delhi, New Delhi, Hauzkhas-110016 New Delhi, Hauzkhas-110016 [email protected] [email protected] Abstract compositionality: evident by the choice of the for- mal language and the construction mechanism and Different from previous surveys in seman- lexical variation: grounding the words/phrases to tic parsing (Kamath and Das, 2018) and appropriate KB entity/relation. knowledge base question answering(KBQA) (Chakraborty et al., 2019; Zhu et al., 2019; Language Compositionality: According to Hoffner¨ et al., 2017) we try to takes a dif- Frege’s principle of compositionality: “the ferent perspective on the study of semantic meaning of a whole is a function of the meaning parsing. Specifically, we will focus on (a) meaning composition from syntactical struc- of the parts. The study of formal semantics ture (Partee, 1975), and (b) the ability of for natural language to appropriately represent semantic parsers to handle lexical variation sentence meaning has a long history (Szab, 2017). given the context of a knowledge base (KB). Most semantic formalism follow the tradition of In the following section after an introduction Montague’s grammar (Partee, 1975) i.e. there is of the field of semantic parsing and its uses a one-to-one correspondence between syntax and in KBQA, we will describe meaning represen- semantics e.g. CCG (Zettlemoyer and Collins, tation using grammar formalism CCG (Steed- 2005). We will not be delving into the concept of man, 1996).
    [Show full text]
  • Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
    Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training Peng Shi1*, Patrick Ng2, Zhiguo Wang2, Henghui Zhu2, Alexander Hanbo Li2, Jun Wang2, Cicero Nogueira dos Santos2, Bing Xiang2 1 University of Waterloo, 2 AWS AI Labs [email protected], fpatricng,zhiguow,henghui,hanboli,juwanga,cicnog,[email protected] Abstract Pain Point 1: Fail to match and detect the column mentions. Utterance: Which professionals live in a city containing the Most recently, there has been significant interest in learn- substring ’West’? List his or her role, street, city and state. ing contextual representations for various NLP tasks, by Prediction: SELECT role code, street, state leveraging large scale text corpora to train large neural lan- FROM Professionals WHERE city LIKE ’%West%’ guage models with self-supervised learning objectives, such Error: Missing column city in SELECT clause. as Masked Language Model (MLM). However, based on a pi- lot study, we observe three issues of existing general-purpose Pain Point 2: Fail to infer columns based on cell values. language models when they are applied to text-to-SQL se- Utterance: Give the average life expectancy for countries in mantic parsers: fail to detect column mentions in the utter- Africa which are republics? ances, fail to infer column mentions from cell values, and fail Prediction: SELECT Avg(LifeExpectancy) FROM to compose complex SQL queries. To mitigate these issues, country WHERE Continent = ’Africa’ we present a model pre-training framework, Generation- Error: Missing GovernmentForm = ’Republic’. Augmented Pre-training (GAP), that jointly learns repre- Pain Point 3 sentations of natural language utterances and table schemas : Fail to compose complex target SQL.
    [Show full text]
  • On the Use of Parsing for Named Entity Recognition
    applied sciences Review On the Use of Parsing for Named Entity Recognition Miguel A. Alonso * , Carlos Gómez-Rodríguez and Jesús Vilares Grupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, Spain; [email protected] (C.G.-R.); [email protected] (J.V.) * Correspondence: [email protected] Abstract: Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task. Keywords: natural language processing; named entity recognition; parsing; sequence labeling Citation: Alonso, M.A; Gómez- 1.
    [Show full text]
  • Neural Semantic Parsing for Syntax-Aware Code Generation
    Neural Semantic Parsing for Syntax-Aware Code Generation Masterarbeit zur Erlangung des Master-Grades Master of Science im Studiengang Tech- nische Informatik an der Fakultät für Informations-, Medien- und Elektrotechnik der Technischen Hochschule Köln vorgelegt von: Artur Baranowski Matrikel-Nr.: 11130536 Adresse: Heidestraße 144 51147 Köln Erstprüfer: Prof. Dr. Hubert Randerath Zweitprüfer: Prof. Dr. Nico Hochgeschwender Köln, 23. September 2020 Abstract I Abstract The task of mapping natural language expressions to logical forms is referred to as semantic parsing. The syntax of logical forms that are based on programming or query languages, such as Python or SQL, is defined by a formal grammar. In this thesis, we present an efficient neural semantic parser that exploits the underlying grammar of logical forms to enforce well-formed expressions. We use an encoder-decoder model for sequence prediction. Syntactically valid programs are guaranteed by means of a bottom- up shift-reduce parser, that keeps track of the set of viable tokens at each decoding step. We show that the proposed model outperforms the standard encoder-decoder model across datasets and is competitive with comparable grammar-guided semantic parsing approaches. Keywords: semantic parsing, machine learning, natural language processing, code generation, formal grammars, parsing theory Statutory Declaration II Statutory Declaration I herewith declare that I have composed the present thesis myself and without use of any other than the cited sources and aids. Sentences or parts of sentences quoted literally are marked as such; other references with regard to the statement and scope are indicated by full details of the publications concerned. The thesis in the same or similar form has not been submitted to any examination body and has not been published.
    [Show full text]