In this paper, we present an open-source parsing environment (Tuebingen Linguistic Parsing Architecture, TuLiPA) which uses Range Concatenation Grammar (RCG) as a pivot formalism, thus opening the way to the parsing of several mildly context-sensitive formalisms. This environment currently supports tree-based grammars (namely Tree-Adjoining Grammars, TAG) and Multi-Component Tree-Adjoining Grammars with Tree Tuples (TT-MCTAG)) and allows computation not only of syntactic structures, but also of the corresponding semantic representations. It is used for the development of a tree-based grammar for German.
University of Montpellier（蒙彼利埃大学）；CNRS（法国国家科学研究中心）
The short note describes the chart parser for multimodal type-logical grammars which has been developed in conjunction with the type-logical treebank for French. The chart parser presents an incomplete but fast implementation of proof search for multimodal type-logical grammars using the "deductive parsing" framework. Proofs found can be transformed to natural deduction proofs.
Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries. Meanwhile, how to better align SQL clauses and question segments has been a key challenge for parsing performance. Therefore, this paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experimental results of two parsers show that our method obtains consistent improvemen…
Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing algorithms are respectively proposed for two converted tree representations, division span and joint span. As HPSG encodes both constituent and dependency structure information, the proposed HPSG parsers may be regarded as a sort of joint decoder for both types of structures and…
Linköping University（林雪平大学）；University of Padua（帕多瓦大学）
We study the parsing complexity of Combinatory Categorial Grammar (CCG) in the formalism of Vijay-Shanker and Weir (1994). As our main result, we prove that any parsing algorithm for this formalism will take in the worst case exponential time when the size of the grammar, and not only the length of the input sentence, is included in the analysis. This sets the formalism of Vijay-Shanker and Weir (1994) apart from weakly equivalent formalisms such as Tree-Adjoining Grammar (TAG), for which parsing can be performed in time polynomial in the combined size of grammar and input sentence. Our result…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；McGill University（麦吉尔大学）；Stanford University（斯坦福大学）
This technical report presents a general framework for parsing a variety of grammar formalisms. We develop a grammar formalism, called an Abstract Grammar, which is general enough to represent grammars at many levels of the hierarchy, including Context Free Grammars, Minimalist Grammars, and Generalized Context-free Grammars. We then develop a single parsing framework which is capable of parsing grammars which are at least up to GCFGs on the hierarchy. Our parsing framework exposes a grammar interface, so that it can parse any particular grammar formalism that can be reduced to an Abstract Gra…
Samsung AI Centre（三星人工智能中心）；Peak AI；University of Edinburgh（爱丁堡大学）
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the linearized graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be well-formed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that ensures only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on th…
This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches. The report also documents a 20-year research program, showing how NLP methods have evolved over this time. The staggering accuracy improvements provided by neural models for CCG parsing can be seen as a reflection of the improvements seen in NLP more generally. The report provides a minimal introduction to CCG and CCG parsing, with many pointers to the relevant literatu…
To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-o…
SYNTAGMA is a rule-based parsing system, structured on two levels: a general parsing engine and a language specific grammar. The parsing engine is a language independent program, while grammar and language specific rules and resources are given as text files, consisting in a list of constituent structuresand a lexical database with word sense related features and constraints. Since its theoretical background is principally Tesniere's Elements de syntaxe, SYNTAGMA's grammar emphasizes the role of argument structure (valency) in constraint satisfaction, and allows also horizontal bounds, for ins…
Synchronous Context-Free Grammars and Optimal Linear Parsing Strategies
Università di Firenze（佛罗伦萨大学）；University of Rochester（罗切斯特大学）；Università di Milano（米兰大学）；Università di Roma Tor Vergata（罗马第二大学（托尔·维尔加塔））；University of Padua（帕多瓦大学）
Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sample…
University of Edinburgh（爱丁堡大学）；University of Rochester（罗切斯特大学）
We describe a matrix multiplication recognition algorithm for a subset of binary linear context-free rewriting systems (LCFRS) with running time $O(n^{ωd})$ where $M(m) = O(m^ω)$ is the running time for $m \times m$ matrix multiplication and $d$ is the "contact rank" of the LCFRS -- the maximal number of combination and non-combination points that appear in the grammar rules. We also show that this algorithm can be used as a subroutine to get a recognition algorithm for general binary LCFRS with running time $O(n^{ωd + 1})$. The currently best known $ω$ is smaller than $2.38$. Our result provi…
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars
Department of Computer Science, Faculty of Computers and Information, Cairo University（计算机科学系，计算机与信息学院，开罗大学）；Department of EECE, Faculty of Engineering, Cairo University（电子工程系，工程学院，开罗大学）；Department of Arabic Language and Literatures, Faculty of Arts, Cairo University（阿拉伯语言与文学系，艺术学院，开罗大学）
Salesforce Research（赛富时研究院）；Yale University（耶鲁大学）；University of Edinburgh（爱丁堡大学）；Facebook AI（脸书人工智能研究院）
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG) induced from existing text-to-SQL datasets. We pre-train our model on the synthetic data using a novel text-schema linking objective that predicts the syntactic role of a table field in the SQL for each question-SQL pair. To maintain the model's ability to represent real-world data, we also include maske…
The intersection of Finite State Automata and Definite Clause Grammars
Bernard Lang将解析定义为计算FSA（输入）和CFG的交集。将用于解析的输入视为FSA而不是字符串与语音理解系统中的一些方法很好地结合，在语音理解系统中，解析将词格（而不是词串）作为输入。此外，某些技术的鲁棒解析可以建模为有限状态转换器。在本文中，我们将探讨如何推广这种方法的统一文法。特别是，我们将集中在我们如何可能的FSA和DCG的交集的计算。它表明，现有的解析算法可以很容易地扩展FSA输入。然而，我们也表明，终止属性发生了巨大的变化：我们表明，它是不可判定的FSA和DCG的交集是否为空（即使DCG是离线解析）。此外，我们讨论的方法来处理这个问题。
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Pompeu Fabra University（庞培法布拉大学）
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better language modeling than state-of-the-art sequential RNNs in English and Chinese.
The LL(finite) parsing strategy for parsing of LL(k) grammars where k needs not to be known is presented. The strategy parses input in linear time, uses arbitrary but always minimal lookahead necessary to disambiguate between alternatives of nonterminals, and it is optimal in the number of lookahead terminal scans performed. Modifications to the algorithm are shown that allow for resolution of grammar ambiguities by precedence -- effectively interpreting the input as a parsing expression grammar -- as well as for the use of predicates, and a proof of concept, the open-source parser generator A…
Universidade da Coruña（拉科鲁尼亚大学）；CITIC（信息与通信技术研究中心）
In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequenc…
