A Lexicalized Tree-Adjoining Grammar for Vietnamese

In this paper, we present the first sizable grammar built for Vietnamese using LTAG, developed over the past two years, named vnLTAG. This grammar aims at modelling written language and is general enough to be both application- and domain-independent. It can be used for the morpho-syntactic tagging and syntactic parsing of Vietnamese texts, as well as text generation. We then present a robust parsing scheme using vnLTAG and a parser for the grammar. We finish with an evaluation using a test suite

A uniform architecture for parsing and generation

The use of a single grammar for both parsing and generation is an idea with a certain elegance, the desirability of which several researchers have noted. In this paper, we discuss a more radical possibility: not only can a single grammar be used by different processes engaged in various "directions" of processing, but one and the same language-processing architecture can be used for processing the grammar in the various modes. In particular, parsing and generation can be viewed as two processes engaged in by a single parameterized theorem prover for the logical interpretation of the formalism. We discuss our current implementation of such an architecture, which is parameterized in such a way that it can be used for either purpose with grammars written in the PATR formalism. Furthermore, the architecture allows fine tuning to reflect different processing strategies, including parsing models intended to mimic psycholinguistic phenomena. This tuning allows the parsing system to operate within the same realm of efficiency as previous architectures for parsing alone, but with much greater flexibility for engaging in other processing regimes.Engineering and Applied Science

A Lexicalized Tree-Adjoining Grammar for Vietnamese

In this paper, we present the first sizable grammar built for Vietnamese using LTAG, developed over the past two years, named vnLTAG. This grammar aims at modelling written language and is general enough to be both application- and domain-independent. It can be used for the morpho-syntactic tagging and syntactic parsing of Vietnamese texts, as well as text generation. We then present a robust parsing scheme using vnLTAG and a parser for the grammar. We finish with an evaluation using a test suite

Building a robust dialogue system with limited data

We describe robustness techniques used in the CommandTalk system at the recognition level, the parsing level, and th dia6ue level, and how these were influenced by the lack of domain data. We used interviews with subject matter experts (SME's) to develop a single grammar for recognition, understanding, and generation, thus eliminating the need for a robust parser. We broadened the coverage of the recognition grammar by allowing word insertions and deletions, and we implemented clarification and correction subdialogues to increase robustness at tte dialogue level. We discuss the applicability of these techniques to other domains

A Contrastive Study of Functional Unification Grammar for Surface Language Generation: A Case Study in Choice of Connectives

Language generation systems have used a variety of grammatical formalisms for producing syntactic structure and yet, there has been little research evaluating the formalisms for the specifics of the generation task. In our work at Columbia we have primarily used a unification based formalism, a Functional Unification Grammar (FUG) [Kay 79] and have found it well suited for many of the generation tasks we have addressed. Over the course of the past 5 years we have also explored the use of various off-the-shelf parsing formalisms, including an Augmented Transition Network (ATN) [Woods 701], a Bottom-Up Chart Parser (SUP) [Finin 84], and a Declarative Clause Grammar (DCG) [Pereira and Warren 80]. In contrast, we have found that parsing formalisms do not have the same benefits for the generation task

Comparison of Surface Language Generators: A Case Study in Choice of Connectives

Language generation systems have used a variety of grammatical formalisms for producing syntactic structure and yet, there has been little research evaluating the formalisms for the specifics of the generation task. In our work at Columbia we have primarily used a unification based formalism, a Functional Unification Grammar (FUG) [Kay 79] and have found it well suited for many of the generation tasks we have addressed. Over the course of the past 5 years we have also explored the use of various off-the-shelf parsing formalisms, including an Augmented Transition Network (ATN) [Woods 70]. a Bottom-Up Chan Parser (BUP) [Finin 84], and a Declarative Clause Grammar (DCG) [Pereira & Warren 80]. In this paper, we identify the characteristics of FDG that we find useful for generation and contrast these with characteristics of the parsing formalisms and with other formalisms that are typically used for generation

Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning

Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output format exactly. To address this issue, grammar-constrained decoding (GCD) can be used to control the generation of LMs, guaranteeing that the output follows a given structure. Most existing GCD methods are, however, limited to specific tasks, such as parsing or code generation. In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general. For increased flexibility, we introduce input-dependent grammars, which allow the grammar to depend on the input and thus enable the generation of different output structures for different inputs. We then empirically demonstrate the power and flexibility of GCD-enhanced LMs on (1) information extraction, (2) entity disambiguation, and (3) constituency parsing. Our results indicate that grammar-constrained LMs substantially outperform unconstrained LMs or even beat task-specific finetuned models. Grammar constraints thus hold great promise for harnessing off-the-shelf LMs for a wide range of structured NLP tasks, especially where training data is scarce or finetuning is expensive. Code and data: https://github.com/epfl-dlab/GCD.Comment: Accepted at EMNLP 2023 Main Conferenc

A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar

International audienceSurface realisers divide into those used in generation (NLG geared realisers) and those mirroring the parsing process (Reversible realisers). While the first rely on grammars not easily usable for parsing, it is unclear how the second type of realisers could be parameterised to yield from among the set of possible paraphrases, the paraphrase appropriate to a given generation context. In this paper, we present a surface realiser which combines a reversible grammar (used for parsing and doing semantic construction) with a symbolic means of selecting paraphrases

Comparison of Context-free Grammars Based on Parsing Generated Test Data

There exist a number of software engineering scenarios that essentially involve equivalence or correspondence assertions for some of the context-free grammars in the scenarios. For instance, when applying grammar transformations during parser development—be it for the sake of disambiguation or grammar-class compliance—one would like to preserve the generated language. Even though equivalence is generally undecidable for context-free grammars, we have developed an automated approach that is practically useful in revealing evidence of nonequivalence of grammars and discovering correspondence mappings for grammar nonterminals. Our approach is based on systematic test data generation and parsing. We discuss two studies that show how the approach is used in comparing grammars of open source Java parsers as well as grammars from the course work for a compiler construction class