Abstract

We present JSrealB, an English and French text realizer written in JavaScript to ease its integration in web applications. The realization engine is mainly rule-based. Table driven rules are defined for inflection and algorithmic propagation rules, for agreements. It allows its user to build a variety of French and English expressions and sentences from a single specification to produce dynamic output depending on the content of a web page. It has been integrated into a meteorological information display system, but there is still work to do in order to get a full bilingual realizer.

Introduction

Natural language generation can automate a significant part of textual production, only requiring a human to supply some important aspects and thus saving considerable time for producing consistent grammatically correct output. In recent years, tools such as SimpleNLG (Gatt and Reiter, 2009) facilitated text realization by a programmer provided they program their application in Java. This system was then extended with SimpleNLG-EnFr (Vaudry and Lapalme, 2013), a English-French version of SimpleNLG.

Another approach to text realization is JSreal (Daoust and Lapalme, 2014), a French Web realizer written in JavaScript.  This paper describes an attempt at combining the ideas of SimpleNLG-EnFr and JSreal to produce a bilingual realizer for French and English from a single specification. JSrealB generates well-formed expressions and sentences. It can be used standalone for linguistic demonstrations or be integrated into complex text generation projects. But like JSreal, it is aimed at web developers, from taking care of morphology, declension and conjugation to creating well-formed texts. A web programmer who wishes to use JSrealB to produce flexible English and/or French textual or HTML output only needs to add two lines in the page: one for importing program and one for calling JSrealB loader to load the resources (i.e. lexicon and rules).

The principles underlying JSrealB are similar to those of SimpleNLG: programming language instructions create data structures corresponding to the constituents of the sentence to be produced. Once the data structure (a tree) is built in memory, it is traversed to produce the list of tokens of the sentence. This data structure is built by function calls whose names are the same as the symbols usually used for classical syntax trees: for example, N to create a noun structure, NP for a Noun Phrase, V for a Verb, D for a determiner, S for a Sentence and so on. Features added to the structures using the dot notation can modify the values according to what is intended.

Theoretically, JSrealB requires no knowledge in computer science, its syntactic representation being patterned after classical constituent grammar notations. For example,

S(
  NP(D("a"),N("woman")).n("p"),
  VP("eat").t("ps")
)

is the JSrealB specification for The women ate. Plural is indicated with feature n("p") where n indicates number and p plural. The verb is conjugated to past tense indicated by the feature tense t and value ps. Agreement between NP and VP is performed automatically.

Bilingual realization

French and English are languages whose structures are similar. Both languages use the same alphabet, they are both fusional languages sharing a similar conjugation system and their word order follows the same basic Subject-Verb-Object paradigm (Shoebottom, 1996).

Structural differences do exist: in English, the adjective normally goes before the noun it qualifies, while in French it can appear before or after depending on the meaning (e.g. in French, we say un grand homme for a great man and un homme grand for a big man). In French, nouns agree in gender and number (e.g. un ami in masculine singular, des amies with final es in feminine plural), in English they agree only in numbers (e.g. friend become friends in plural). French determiners are based on the gender and number of the noun they are associated with, and adjectives agree with the nouns they qualify but not in English.

These differences must be taken into account at many levels. First, syntactic differences and agreements (i.e. features propagation), must be handled at the phrase or sentence level by algorithms. For French complex rules, we followed "Le bon usage, grammaire française" (Grevisse, 1980). For English, we relied on various sources from the web.

JSrealB lexicons are based on the ones found in SimpleNLG-EnFr (Vaudry and Lapalme, 2013) These lexicons can be completed by the user to add domain-specific vocabularies. In lexicons, words have grammatical properties (e.g. category, gender, etc.). and a link to an inflection table. Tables are defined for nouns, adjective, verbs and pronouns in both English and French. These inflection rules are language specific and correspond to the information found in (Bescherelle, 2012) and (Delaunay and Laurent, 2013). These conjugation, declension or transformation tables are included with the lexicon in JSrealB, they are defined declaratively for each language and interpreted by a rule engine common to both languages.

Methodology

Our goal is to develop an English and French  text realizer with minimal specific adaptations to each language. We have promoted the systematic application of rules hoping it is possible to support other languages at limited cost.

Architecture

JSreal organization was closely bound to French, so we recognized that it was very hard to adapt JSreal for bilingualism by refactorisation.

We then completely revised its architecture and particularly the rules implementation. So we decided to code transformation rules in tables so that it works for most fusional languages. We aimed at developping a modular program to allow evolution towards to other languages, all modules being hidden behind a specific facade.

A JSrealB user can create a syntactic representation by using a facade which analyzes syntax and propagates features between phrases and from phrases to terminal elements. With an inheritance system, we can use the same propagation scenario or define a special scenario for each language. We can do the same for grammatical function identification and word ordering.

This facade handles the JSrealB core, which is divided into modules to provide better scalability. Basic operations like conjugation, declension, and punctuation are common to fusional languages. We can then define modules to be used by all languages with input parameters that differ such as lexicon and tables.

Syntactic realization

Text realization uses a syntactic hierarchical tree representation that creates a sentence by combining phrases and terminals. The relations between these lexical units determine the propagation of features between words for determining proper agreements. For example, in

S(NP(D("le"),
     N("monsieur").n("p")),
  VP(V("avoir").t("i"),
     NP(D("un"),N("souris")))
)

grammatical categories of words are already specified in the syntactic representation. Word order usually follows the left to right order of the terminals in the tree except in some coordinated sentences where position of coordinate must be determined.

The relations between non-terminals specified in the input determine the grammatical functions of each element, which are roughly similar between French and English.

Figure 1 - Similar syntactic representation for both French and English, only the vocabulary differs.