GenI: Natural language generation in Haskell

In this article we present GenI, a chart based surface realisation tool implemented in Haskell. GenI takes as input a set of first order terms (the input semantics) and a grammar for a given target language (e.g., English, French, Spanish, etc.) and generates sentences in the target language, whose semantic meaning corresponds to the input semantics. The aim of the article is not so much to present GenI or to describe how it is implemented. Rather, we will focus on the aspects of functional programming (higher order functions, monads) and Haskell (typeclasses) that we found important to its design

Adapting polarised disambiguation to surface realisation

The aim of surface realisation is to produce a string from an input semantics. The task may be viewed as the inverse of parsing, and like parsing, it is made more difficult by the problem of lexical ambiguity in natural language. (Bonfante, Guillaume, and Perrier 2004) propose a polarisation technique for parsing that greatly reduces the effects of ambiguity. We show how polarisation can be adapted for surface realisation and how to address two complications which arise: multi and zero-literal semantic input

Discourse parsing for multi-party chat dialogues

In this paper we present the first ever, to the best of our knowledge, discourse parser for multi-party chat dialogues. Discourse in multi-party dialogues dramatically differs from monologues since threaded conversations are commonplace rendering prediction of the discourse structure compelling. Moreover, the fact that our data come from chats renders the use of syntactic and lexical information useless since people take great liberties in expressing themselves lexically and syntactically. We use the dependency parsing paradigm as has been done in the past (Muller et al., 2012; Li et al., 2014). We learn local probability distributions and then use MST for decoding. We achieve 0.680 F 1 on unlabelled structures and 0.516 F 1 on fully labeled structures which is better than many state of the art systems for monologues, despite the inherent difficulties that multi-party chat dialogues have

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

GenI, un réalisateur basé sur une grammaire réversible

National audienceEn génération, un réalisateur de surface a pour fonction de produire, à partir d'une représentation conceptuelle donnée, une phrase grammaticale. Les réalisateur existants soit utilisent une grammaire réversible et des méthodes statistiques pour déterminer parmi l'ensemble des sorties produites la plus plausible; soit utilisent des grammaires spécialisées pour la génération et des méthodes symboliques pour déterminer la paraphrase la plus appropriée à un contexte de génération donné. Dans cet article, nous présentons GenI, un réalisateur de surface basé sur une grammaire d'arbres adjoints pour le français qui réconcilie les deux approches en combinant une grammaire réversible avec une sélection symbolique des paraphrases

Three reasons to adopt TAG-based surface realisation

Surface realisation from flat semantic formulae is known to be exponential in the length of the input. In this paper, we argue that TAG naturally supports the integration of three main ways of reducing complexity: polarity filtering, delayed adjunction and empty semantic items elimination. We support these claims by presenting some preliminary results of the TAG-based surface realiser

The TUNA challenge 2008 : overview and evaluation results

The TUNA Challenge was a set of three shared tasks at REG '08, all of which used data from the TUNA Corpus. The three tasks covered attribute selection for referring expressions (TUNA-AS), realisation (TUNA-R) and end-to-end referring expression generation (TUNA-REG). 8 teams submitted a total of 33 systems to the three tasks, with an additional submission to the Open Track. The evaluation used a range of automatically computed measures. In addition, an evaluation experiment was carried out using the peer outputs for the TUNA-REG task. This report describes each task and the evaluation methods used, and presents the evaluation results.peer-reviewe

Which bridges for bridging definite descriptions?

Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a corpus study of bridging definite descriptions in the french corpus PAROLE. It proposes a typology of bridging relations; describes a system for annotating NPs which allows for a user friendly collection of all relevant information on the bridging definite descriptions occurring in the corpus and discusses the results of the corpus stud

The KBGen Challenge

International audienceGiven a preselected set of relations extracted from the AURA knowledge base on biology, the KBGEN Task consisted in generating a sentence verbalising these relations. Three team submitted the results of their systems. The systems were compared using both automatic metrics (BLEU, NIST) and subjective ratings by 12 human users for three dimensions namely, fluency, grammaticality and meaning similarity. In this report, we summarise the KBGen Task, the evaluation methods and the results obtained

SemTAG, the LORIA toolbox for TAG-based Parsing and Generation

In this paper, we introduce SemTAG, a toolbox for TAG-based parsing and generation. This environment supports the development of wide-coverage grammars and differs from existing environments for TAG such as XTAG, in that it includes a semantic dimension. SemTAG is open-source and freely available