Semantic Parser

Human understanding of spoken language ap- pears to integrate the use of contextual ex- pectations with acoustic level perception in a tightly-coupled, sequential fashion. Yet com- puter speech understanding systems …...

1 The talk will describe an ongoing project (modestly named the Neural Theory of Language) that is attempting to model language behavior in a way that is both neurally plausible …...

We present a novel statistical approach to semantic parsing, WASP, for construct- ing a complete, formal meaning represen- tation of a sentence. A semantic parser is learned given a set …...

This article describes a robust seman- tic parser that uses a broad knowledge base created by interconnecting three ma- jor resources: FrameNet, VerbNet and PropBank. The FrameNet corpus con- tains …...

This paper explores the use of statisti- cal machine translation (SMT) methods for tactical natural language generation. We present results on using phrase-based SMT for learning to map meaning repre- …...

This paper presents the first empirical results to our knowledge on learning synchronous grammars that generate logical forms. Using statistical machine translation techniques, a semantic parser based on a synchronous …...

A semantic parser learning system learns to map natural language sentences into their domain-specific formal meaning rep- resentations, but if the constructs of the meaning representation language do not correspond …...

We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms …...

Compositional question answering begins by mapping questions to logical forms, but train- ing a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. …...

Short listings such as classified ads or product listings abound on the web. If a computer can reliably extract information from them, it will greatly benefit a variety of applications. …...

AbstractSemantic representation of text is key to text understanding and reasoning. In this paper, we present Polaris, Lymba's semantic parser. Polaris is a supervised semantic parser that given text extracts …...

We propose a new measure of semantic similarity between words in context, which exploits the syntactic/semantic structure of the context surrounding each target word. For a given pair of target …...

We present a method for training a semantic parser using only a knowledge base and an un- labeled text corpus, without any individually annotated sentences. Our key observation is that …...

Supervised training procedures for seman- tic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they …...

We consider the challenge of learning seman- tic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety …...

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we …...

“Deep-syntactic” dependency structures that capture the argumentative, attributive and coordi- native relations between full words of a sentence have a great potential for a number of NLP- applications. The abstraction …...

A typical knowledge-based question an- swering (KB-QA) system faces two chal- lenges: one is to transform natural lan- guage questions into their meaning repre- sentations (MRs); the other is to …...

AbstractSemantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning …...

We present a corpus of 2,380 natural language queries paired with machine readable formu- lae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We …...

AbstractWe introduce a new approach to training a semantic parser that uses textual entailment judgements as supervision. These judgements are based on high-level inferences about whether the meaning of one …...

We introduce several probabilistic models for learning the lexicon of a semantic parser. Lex- icon learning is the first step of training a se- mantic parser for a new application …...

AbstractOur goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, …...

AbstractNatural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of …...

AbstractTo 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 …...

AbstractWe evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and …...

AbstractWe present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We …...

AbstractSemantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. …...

One key challenge for building a semantic parser in new domains is the difficulty to an- notate new datasets. In this paper, we pro- pose a sequential transfer learning method …...

AbstractState-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to …...

AbstractWe consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from …...

AbstractTraining a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process …...

AbstractDespite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest …...

AbstractModels of human sentence processing effort tend to focus on costs associated with retrieving structures and discourse referents from memory (memory-based) and/or on costs associated with anticipating upcoming words and …...

AbstractRecent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Real-world regexes …...

AbstractThe importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming …...

AbstractWe present the first fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures (DRSs) that is competitive in accuracy with recent sequence-to-sequence models and at the …...

AbstractEven though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, …...

AbstractRobust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one language, we can …...

AbstractLocalizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how …...