International audience

Previous work on multilingual generation from Abstract Meaning Representations has mostly focused on High-and Medium-Resource languages relying on large amounts of training data.In this work, we consider both Highand Low-Resource languages capping training data size at the lower bound set by our Low-Resource languages i.e., 31K training instances.We propose two straightforward techniques to enhance generation results on Low-Resource while preserving performance on High-and Medium-Resource languages.First, we iteratively refine a multilingual model to a set of monolingual models using Low-Rank Adaptation -this enables cross-lingual transfer while reducing over-fitting for High-Resource languages as the monolingual models are trained last.Second, we base our training curriculum on a tree structure which permits investigating how the languages used at each iteration impact generation performance on High and Low-Resource languages.We show an improvement over both mono and multilingual approaches.Comparing different ways of grouping languages at each iteration step we find two beneficial configurations: grouping related languages which promotes transfer, or grouping distant languages which facilitates regularisation.

William Soto Martinez, Yannick Parmentier, Claire Gardent. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Abstract Arabic presents many challenges for automatic processing. Although several research studies have addressed some issues, electronic resources for processing Arabic remain relatively rare or not widely available. In this paper, we propose a Tree-adjoining grammar with a syntax-semantic interface. It is applied to the modern standard Arabic, but it can be easily adapted to other languages. This grammar named “ArabTAG V2.0” (Arabic Tree Adjoining Grammar) is semi-automatically generated by means of an abstract representation called meta-grammar. To ensure its development, ArabTAG V2.0 benefits from a grammar testing environment that uses a corpus of phenomena. Further experiments were performed to check the coverage of this grammar as well as the syntax-semantic analysis. The results showed that ArabTAG V2.0 can cover the majority of syntactical structures and different linguistic phenomena with a precision rate of 88.76%. Moreover, we were able to semantically analyze sentences and build their semantic representations with a precision rate of about 95.63%.

Abstract The metrics standardly used to evaluate Natural Language Generation (NLG) models, such as BLEU or METEOR, fail to provide information on which linguistic factors impact performance. Focusing on Surface Realization (SR), the task of converting an unordered dependency tree into a well-formed sentence, we propose a framework for error analysis which permits identifying which features of the input affect the models’ results. This framework consists of two main components: (i) correlation analyses between a wide range of syntactic metrics and standard performance metrics and (ii) a set of techniques to automatically identify syntactic constructs that often co-occur with low performance scores. We demonstrate the advantages of our framework by performing error analysis on the results of 174 system runs submitted to the Multilingual SR shared tasks; we show that dependency edge accuracy correlate with automatic metrics thereby providing a more interpretable basis for evaluation; and we suggest ways in which our framework could be used to improve models and data. The framework is available in the form of a toolkit which can be used both by campaign organizers to provide detailed, linguistically interpretable feedback on the state of the art in multilingual SR, and by individual researchers to improve models and datasets.1

Multiword expressions (MWEs) are pervasive in natural languages and often have both idiomatic and compositional readings, which leads to high syntactic ambiguity. We show that for some MWE types idiomatic readings are usually the correct ones. We propose a heuristic for an A* parser for Tree Adjoining Grammars which benefits from this knowledge by promoting MWE-oriented analyses. This strategy leads to a substantial reduction in the parsing search space in case of true positive MWE occurrences, while avoiding parsing failures in case of false positives.

Property Grammar (PG) is a formalism introduced by Blache [1], which aims at describing syntax in terms of local constraints that can be independently violated. A promising feature of this formalism lies in its ability to account for ungrammatical utterances, thus departing from classical formalisms of generative-enumerative syntax. In this article, we present a model-theoretic description of PG that improves on previous work by providing support for properties augmented with feature constraints. (e.g. the requirement and agreement properties). While providing a formal definition of the semantics of feature-based PG, we illustrate various uses of features within this formalism and give a general framework to interpret them. In a second part, we show how this formalization of PG can be turned into a Constraint Optimization Problem to implement a PG parser that supports the computation of both syntactic trees (for grammatical sentences) and quasi-syntactic trees (i.e. linguistically motivated syntactic structure for ungrammatical utterances). Finally, we briefly report on the implementation of such a parser using the Gecode library for Constraint Programming.

In this article, we introduce eXtensible MetaGrammar (XMG), a framework for specifying tree-based grammars such as Feature-Based Lexicalized Tree-Adjoining Grammars (FB-LTAG) and Interaction Grammars (IG). We argue that XMG displays three features that facilitate both grammar writing and a fast prototyping of tree-based grammars. Firstly, XMG is fully declarative. For instance, it permits a declarative treatment of diathesis that markedly departs from the procedural lexical rules often used to specify tree-based grammars. Secondly, the XMG language has a high notational expressivity in that it supports multiple linguistic dimensions, inheritance, and a sophisticated treatment of identifiers. Thirdly, XMG is extensible in that its computational architecture facilitates the extension to other linguistic formalisms. We explain how this architecture naturally supports the design of three linguistic formalisms, namely, FB-LTAG, IG, and Multi-Component Tree-Adjoining Grammar (MC-TAG). We further show how it permits a straightforward integration of additional mechanisms such as linguistic and formal principles. To further illustrate the declarativity, notational expressivity, and extensibility of XMG, we describe the methodology used to specify an FB-LTAG for French augmented with a unification-based compositional semantics. This illustrates both how XMG facilitates the modeling of the tree fragment hierarchies required to specify tree-based grammars and of a syntax/semantics interface between semantic representations and syntactic trees. Finally, we briefly report on several grammars for French, English, and German that were implemented using XMG and compare XMG with other existing grammar specification frameworks for tree-based grammars.