Semantic Parsing

The work presented here is a preliminary sn~y concerning the automatic translation of French natural language statements into the RESEDA seman- tic metalanguage. The text in natural language is first …...

This paper presents a novel method of semantic parsing that maps a natural language (NL) sentence to a logical form. We propose a semantic parsing method by conducting separately two …...

. This paper introduced a semantic parsing model constructed above a symbolic system of concepts for understanding natural language. Based on the model a sentence can be mapped into the …...

. In order to extract some important information of a person from text, an extracting model was proposed. The person’s name is recognized based on the maximal entropy statistic model …...

This paper describes our system for the task of extracting frame semantic structures in SemEval–2007. The system architecture uses two types of learning models in each part of the task: …...

We propose a hybrid genera- tive/discriminative framework for se- mantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM- SVMs). The HVS …...

We present a series of empirical studies aimed at illuminating more precisely the likely contribution of semantic roles in im- proving statistical machine translation ac- curacy. The experiments reported study …...

AbstractThis paper presents a corpus of annotated motion events and their event structure. We consider motion events triggered by a set of motion evoking words and contemplate both literal and …...

AbstractWe present an evaluation framework to enable developers of information seeking, transaction based spoken dialogue systems to compare the robustness of natural language understanding (NLU) approaches across varying levels of …...

Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal forms. Providing this supervision is …...

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the …...

Current approaches for semantic parsing take a supervised approach requiring a consider- able amount of training data which is expen- sive and difficult to obtain. This supervision bottleneck is one …...

This paper introduces tree transducers as a unifying theory for semantic parsing models based on tree transformations. Many exist- ing models use tree transformations, but im- plement specialized training and …...

Existing semantic parsing research has steadily improved accuracy on a few domains and their corresponding databases. This paper introduces FreeParser, a system that trains on one domain and one set …...

AbstractSemantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora …...

Answering natural language questions us- ing the Freebase knowledge base has re- cently been explored as a platform for ad- vancing the state of the art in open do- main …...

When executing commands, a robot has a certain level of contextual knowledge about the environment in which it oper- ates. Taking this knowledge into account can be beneficial to disambiguate …...

AbstractIn this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural …...

AbstractThis paper presents a method to create WordNet-like lexical resources for different languages. Instead of directly translating glosses from one language to another, we perform first semantic parsing of WordNet …...

We present an empirical study on the use of semantic information for Concept Seg- mentation and Labeling (CSL), which is an important step for semantic parsing. We represent the alternative …...

A central challenge in semantic parsing is handling the myriad ways in which knowl- edge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired with …...

We propose a novel model for parsing natural language sentences into their for- mal semantic representations. The model is able to perform integrated lexicon ac- quisition and semantic parsing, mapping …...

In this paper we introduce our system ca- pable of producing semantic parses of sen- tences using three different annotation for- mats. The system was used to partic- ipate in …...

AbstractWe present a new resource, the UnixMan Corpus, for studying language learning it the domain of Unix utility manuals. The corpus is built by mining Unix (and other Unix related) …...

Existing semantic parsing research has steadily improved accuracy on a few do- mains and their corresponding meaning representations. In this paper, we present a novel supervised semantic parsing algo- rithm, …...

This paper presents a semantic parsing and reasoning approach to automatically solving math word problems. A new meaning representation language is de- signed to bridge natural language text and math …...

As a key representation model of knowl- edge, ontology has been widely used in a lot of NLP related tasks, such as se- mantic parsing, information extraction and text mining …...

Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint …...

AbstractWe propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses …...

In this paper, we explore the IBM Model with a ℓ0-norm prior to the se- mantic parsing which parses a sentence to its corresponding meaning representa- tion, and compare two …...

Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, …...

AbstractIn this article, we present the RATP-DECODA Corpus which is composed by a set of 67 hours of speech from telephone conversations of a Customer Care Service (CCS). This corpus …...

Digital personal assistants are becoming both more common and more useful. The major NLP challenge for personal assis- tants is machine understanding: translat- ing natural language user commands into an …...

In this work, we show the possibility of infer- ring the answer type before solving a factoid question and leveraging the type information to improve semantic parsing. By replacing the …...

Semantic parsing aims at mapping nat- ural language to machine interpretable meaning representations. Traditional ap- proaches rely on high-quality lexicons, manually-built templates, and linguis- tic features which are either domain- …...

Meaning Representation parsing James Goodman∗ Andreas Vlachos† Jason Naradowsky∗ ∗ Computer Science Department, University College London james@janigo.co.uk, jason.narad@gmail.com † Department of Computer Science, University of Sheffield a.vlachos@sheffield.ac.uk Abstract Semantic parsers …...

Existing knowledge-based question an- swering systems often rely on small an- notated training data. While shallow meth- ods like relation extraction are robust to data scarcity, they are less expressive …...

We present a novel semi-supervised approach for sequence transduction and apply it to se- mantic parsing. The unsupervised component is based on a generative model in which latent sentences generate …...

A major challenge of semantic parsing is the vocabulary mismatch problem be- tween natural language and target ontol- ogy. In this paper, we propose a sen- tence rewriting based semantic …...

We propose an approach for semantic parsing that uses a recurrent neural net- work to map a natural language question into a logical form representation of a KB query. Building …...

AbstractWe present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on …...

AbstractWe consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation …...

AbstractTasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The …...

AbstractExisting studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and …...

AbstractSemantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be …...

AbstractWe present a simple encoding for unlabeled noncrossing graphs and show how its latent counterpart helps us to represent several families of directed and undirected graphs used in syntactic and …...

AbstractNatural language constitutes a predominant medium for much of human learning and pedagogy. We consider the problem of concept learning from natural language explanations, and a small number of labeled …...

AbstractWe consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically …...

AbstractNeural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus …...

Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch where sentences are aligned with scoped meaning …...

AbstractNeural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence …...

AbstractThe most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist …...

AbstractTraditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, …...

AbstractTechnical support problems are very complex. In contrast to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like …...

AbstractCurrent measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of …...

Semantic parsing targets at mapping human utterances into structured meaning represen- tations, such as logical forms, programming snippets, SQL queries etc. In this work, we focus on logical form generation, …...

AbstractSyntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of …...

AbstractWe describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface. Multiple time series belonging …...

AbstractCombinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire in an unsupervised way. We propose an …...

AbstractReading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of …...

AbstractStructured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and …...

AbstractSemantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing an NL description for some given MRs. Despite …...

AbstractIn many machine learning scenarios, supervision by gold labels is not available and conse quently neural models cannot be trained directly by maximum likelihood estimation. In a weak supervision scenario, …...

AbstractProgrammers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, …...

AbstractSemantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on …...

AbstractAs a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results. In this paper, we propose a new, unified formulation …...

AbstractSemantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs). While neural network-based semantic parsers have achieved impressive improvements over previous methods, results …...

AbstractSemantic parsing converts natural language queries into structured logical forms. The lack of training data is still one of the most serious problems in this area. In this work, we …...

AbstractWe present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1. The encoder is a Bi-LSTM and the decoder uses recursive self-attention. The proposed model alleviates challenges …...

AbstractSemantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains. However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, …...

AbstractMeaning representation is an important component of semantic parsing. Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them. Thus, the impact …...

AbstractThe current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to their deployment for voice assistants …...

AbstractThe structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as …...

AbstractThis paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese. It is ‘general-purpose’ in the sense that it is not tailored to a specific variant of CG, …...

AbstractThe celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving. However, these models either …...

AbstractGeneralization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has …...

AbstractIn this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing. In this architecture, we use an adversarial training method toalign the space of …...

AbstractThe emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. MRs exhibit structural differences that reflect different theoretical …...

AbstractBoth syntactic and semantic structures are key linguistic contextual clues, in which parsing the latter has been well shown beneficial from parsing the former. However, few works ever made an …...

AbstractWhen translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in …...

AbstractThis paper is concerned with semantic parsing for English as a second language (ESL). Motivated by the theoretical emphasis on the learning challenges that occur at the syntax-semantics interface during …...

AbstractDespite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach to encourage compositional behavior in neural models …...

AbstractRecent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not …...

AbstractOne daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an …...

Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling lan- guage. To better understand and leverage what PLMs have learned, several …...

AbstractCode-Mixing (CM) is a common phenomenon in multilingual societies. CM plays a significant role in technology and medical fields where terminologies in the native language are not available or known. …...

AbstractIn this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with k utterance-logical form pairs per new predicate. The …...

AbstractModern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize at test time to new compositions/structures that have …...

AbstractAI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being …...

AbstractSemantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous …...

Target meaning representations for semantic parsing tasks are often based on programming or query languages, such as SQL, and can be formalized by a context-free grammar. As- suming a priori …...

Sequences are typically decoded in a left- to-right fashion, requiring as many decoding steps as there are tokens in the sequence. Recently, several works have proposed non- autoregressive decoders that …...

AbstractSemantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged …...

AbstractSynthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this …...

AbstractSequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to …...

AbstractSemantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational …...

AbstractSemantic parsing maps natural language (NL) utterances into logical forms (LFs), which underpins many advanced NLP problems. Semantic parsers gain performance boosts with deep neural networks, but inherit vulnerabilities against …...

AbstractSemantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To …...

Modern task-oriented semantic parsing ap- proaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models are empirically strong, their specific …...

AbstractSpeech disfluencies are prevalent in spontaneous speech. The rising popularity of voice assistants presents a growing need to handle naturally occurring disfluencies. Semantic parsing is a key component for understanding …...

AbstractDespite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of …...

AbstractThis paper investigates continual learning for semantic parsing. In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previous tasks. Direct application of the …...

AbstractQuestion answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA …...

AbstractSemantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., …...

Abstract“Semantic parsing aims to convert natural language utterances to logical forms. A critical challenge for constructing semantic parsers is the lack of labeled data. In this paper, we propose a …...

AbstractThere is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components. We demonstrate that one …...

AbstractLarge language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as …...

AbstractA common recent approach to semantic parsing augments sequence-to-sequence models by retrieving and appending a set of training samples, called exemplars. The effectiveness of this recipe is limited by the …...

AbstractSemantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence based auto-regressive (AR) approaches are common for conversational SP, recent studies …...

AbstractSeveral studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore …...

AbstractMachine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods predict interpretable but …...

AbstractSemantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a …...

AbstractThe task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully …...

AbstractRecent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations. However, it is still unclear …...

AbstractSemantic parsing considers the task of mapping a natural language sentence into a target formal representation, where various sophisticated sequence-to-sequence (seq2seq) models have been applied with promising results. Generally, these …...

AbstractParsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, …...

AbstractIn this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word …...

AbstractCurrent neural semantic parsers take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. Thus, minimizing the supervision effort is one of …...

AbstractIn order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require generalizing to …...

AbstractSemantic parsing maps natural language questions into logical forms, which can be executed against a knowledge base for answers. In real-world applications, the performance of a parser is often limited …...

AbstractSeq2seq models have been shown to struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions of phenomena that the model handles correctly in isolation. We phrase semantic …...

AbstractExisting neural models have difficulty generalizing to unseen combinations of seen components. To achieve compositional generalization, models are required to consistently interpret (sub)expressions across contexts. Without modifying model architectures, we …...

AbstractModern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated multilingual models are less robust for semantic parsing compared to …...

AbstractIn-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted …...

AbstractThis paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data …...

AbstractIn tasks like semantic parsing, instruction following, and question answering, standard deep networks fail to generalize compositionally from small datasets. Many existing approaches overcome this limitation with model architectures that …...

AbstractSemantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve …...

AbstractCompositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization …...

AbstractWe propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase …...

AbstractSemantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advances in language models trained on code have shown superior performance …...