Surface Realisation from Knowledge Bases. (Bases de Connaissances et Réalisation de Surface)

Surface Realisation from Knowledge Bases Bikash Gyawali Natural Language Generation (NLG) is the task of automatically producing natural language text to describe information present in non-linguistic data. Given some non-linguistic data as input and a defined communicative goal, NLG involves three main tasks: (i) selecting and structuring the relevant portion of input data which addresses the specified communicative goal; (ii) determining the words that will be used to verbalise the selected data; and (iii) mapping these words into a natural language text verbalising the information contained in the selected data. The latter task is known as Surface Realisation (SR) and in this thesis, we study the SR task in the context of input data coming from Knowledge Bases (KB). The motivation for verbalising KB data comes from the need of having human friendly access to such data (practical motive) and of developing generic and linguistically principled approaches for doing so (theoretical motive). We present two novel approaches to surface realisation from knowledge base data: a supervised and a weakly supervised approach. Supervised Approach: The KBGen challenge [Banik et al., 2012, Banik et al., 2013] was designed to compare and evaluate surface realisation systems taking as input knowledge base data. Given a knowledge base fragment which forms a coherent unit, the task was to generate complex sentences which are both grammatical and fluent in English. The challenge made available to the participants a small (207 training examples) parallel corpus of text and KB fragment pairs as well as lexicons mapping KB symbols to words and phrases. In the first part of this thesis, we present a corpus-based method for inducing a Feature Based Lexicalized Tree Adjoining Grammar (FB-LTAG) from a parallel corpus of text and data. The resulting extracted TAG includes a unification based semantics and can be used by an existing surface realiser to generate sentences from KB data. We apply our induction method to the KBGen data, use an existing surface realiser and implement a ranking module to test the resulting grammar on KBGen test data. Experimental evaluation shows that our approach outperforms a data-driven generate-and-rank approach based on an automatically induced probabilistic grammar; and yields results that are close to those produced by a handcrafted symbolic approach. Moreover, a distinguishing feature of our approach is that it relies on an automatically extracted grammar that is compact (a few hundred trees) and linguistically principled (it follows the semantic and extended domain of locality principles of Tree Adjoining Grammar). We show that this feature allows for a hybrid approach where an automatically extracted grammar can be manually revised to improve both coverage and output quality. Weakly Supervised Approach. A strong limitation of the supervised approach just described is that it requires the existence of a parallel corpus aligning a KB fragment with a sentence verbalising that fragment. In the second part of this thesis, we therefore explore an approach for surface realisation from KB data that uses a supplied lexicon but does not require a parallel corpus. Instead, we build a corpus from heterogeneous sources of text related to the domain of the knowledge base for which surface realisation is being developed (in this case, biology) and we use this corpus to identify possible lexicalisations of the KB symbols (classes and relations). We then use this corpus to estimate the probabilities of KB symbol lexicalisations, of subcategorisation frames and of the linking between the various syntactic and semantic arguments of a given event. We propose probabilistic models for the selection of appropriate frames and syntax/semantics mapping and use a scoring function that utilises the learnt probabilities to verbalise the given input. We present automatic and human based evaluations of the output sentences and analyze issues relevant to learning from non-parallel corpora. In both these approaches, we use the KBGen data as a reference input. The KBGen data is itself derived from an existing biomedical ontology (namely, the AURA Knowledge base, [Chaudhri et al., 2013]). Our methods are generic and can be easily adapted for input from other ontologies for which a parallel/non-parallel corpora exists. Je dédie cette thèse à mon temps passé à Nancy.