A syntactic language model based on incremental CCG parsing

Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCGbank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy

A syntactified direct translation model with linear-time decoding

Recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. The decoders accompanying these extensions typically exceed quadratic time complexity. This paper extends the Direct Translation Model 2 (DTM2) with syntax while maintaining linear-time decoding. We employ a linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar (CCG). As every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single supertag–operator pair for extending the dependency parse incrementally. Alongside translation features extracted from the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly outperforms the state-of-the art DTM2 system

Evaluating Scoped Meaning Representations

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 representations in order to capture the semantics of negation, modals, quantification, and presupposition triggers. The semantic formalism is based on Discourse Representation Theory, but concepts are represented by WordNet synsets and thematic roles by VerbNet relations. Translating scoped meaning representations to sets of clauses enables us to compare them for the purpose of semantic parser evaluation and checking translations. This is done by computing precision and recall on matching clauses, in a similar way as is done for Abstract Meaning Representations. We show that our matching tool for evaluating scoped meaning representations is both accurate and efficient. Applying this matching tool to three baseline semantic parsers yields F-scores between 43% and 54%. A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations. This comparison turns out to be an additional way of (i) finding annotation mistakes and (ii) finding instances where our semantic analysis needs to be improved.Comment: Camera-ready for LREC 201

Wide-coverage deep statistical parsing using automatic dependency structure annotation

A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a number of experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit the experiments in Preiss (2003) and Kaplan et al. (2004), this time using the sophisticated automatic LFG f-structure annotation methodologies of Cahill et al. (2002b, 2004) and Burke (2006), with surprising results. We compare various PCFG and history-based parsers (based on Collins, 1999; Charniak, 2000; Bikel, 2002) to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers (Carroll and Briscoe 2002; Riezler et al. 2002). We evaluate using dependency-based gold standards (DCU 105, PARC 700, CBS 500 and dependencies for WSJ Section 22) and use the Approximate Randomization Test (Noreen 1989) to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, widecoverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank (King et al. 2003), a statistically significant improvement of 2.18%over the most recent results of 80.55%for the hand-crafted LFG grammar and XLE parsing system of Riezler et al. (2002), and an f-score of 80.23% against the CBS 500 Dependency Bank (Carroll, Briscoe, and Sanfilippo 1998), a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system of Carroll and Briscoe (2002)

Treebank-based acquisition of wide-coverage, probabilistic LFG resources: project overview, results and evaluation

This paper presents an overview of a project to acquire wide-coverage, probabilistic Lexical-Functional Grammar (LFG) resources from treebanks. Our approach is based on an automatic annotation algorithm that annotates “raw” treebank trees with LFG f-structure information approximating to basic predicate-argument/dependency structure. From the f-structure-annotated treebank we extract probabilistic unification grammar resources. We present the annotation algorithm, the extraction of lexical information and the acquisition of wide-coverage and robust PCFG-based LFG approximations including long-distance dependency resolution. We show how the methodology can be applied to multilingual, treebank-based unification grammar acquisition. Finally we show how simple (quasi-)logical forms can be derived automatically from the f-structures generated for the treebank trees

A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.Comment: Accepted for publication at The 13th International Conference on Computational Semantics (IWCS 2019

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This mapping allows them to effectively leverage the information contained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting---these semantic parsers can only assign meaning to language that falls within the KB's manually-produced schema. Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base. However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models. We show how to combine the disparate representations used by these two approaches, presenting for the first time a semantic parser that (1) produces compositional, executable representations of language, (2) can successfully leverage the information contained in both a formal KB and a large corpus, and (3) is not limited to the schema of the underlying KB. We demonstrate significantly improved performance over state-of-the-art baselines on an open-domain natural language question answering task.Comment: Re-written abstract and intro, other minor changes throughout. This version published at AAAI 201