Abstract: A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.

Introduction: A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and paired texts BIBREF0 , BIBREF1 , BIBREF2 . These correspondences describe how data representations are expressed in natural language (content realisation) but also indicate which subset of the data is verbalised in the text (content selection). Although content selection is traditionally performed by domain experts, recent advances in generation using neural networks BIBREF3 , BIBREF4 have led to the use of large scale datasets containing loosely related data and text pairs. A prime example are online data sources like DBPedia BIBREF5 and Wikipedia and their associated texts which are often independently edited. Another example are sports databases and related textual resources. Wiseman et al. wiseman-shieber-rush:2017:EMNLP2017 recently define a generation task relating statistics of basketball games with commentaries and a blog written by fans. In this paper, we focus on short text generation from such loosely aligned data-text resources. We work with the biographical subset of the DBPedia and Wikipedia resources where the data corresponds to DBPedia facts and texts are Wikipedia abstracts about people. Figure 1 shows an example for the film-maker Robert Flaherty, the Wikipedia infobox, and the corresponding abstract. We wish to bootstrap a data-to-text generator that learns to verbalise properties about an entity from a loosely related example text. Given the set of properties in Figure ( 1 a) and the related text in Figure ( 1 b), we want to learn verbalisations for those properties that are mentioned in the text and produce a short description like the one in Figure ( 1 c). In common with previous work BIBREF6 , BIBREF7 , BIBREF8 our model draws on insights from neural machine translation BIBREF3 , BIBREF9 using an encoder-decoder architecture as its backbone. BIBREF7 introduce the task of generating biographies from Wikipedia data, however they focus on single sentence generation. We generalize the task to multi-sentence text, and highlight the limitations of the standard attention mechanism which is often used as a proxy for content selection. When exposed to sub-sequences that do not correspond to any facts in the input, the soft attention mechanism will still try to justify the sequence and somehow distribute the attention weights over the input representation BIBREF10 . The decoder will still memorise high frequency sub-sequences in spite of these not being supported by any facts in the input. We propose to alleviate these shortcomings via a specific content selection mechanism based on multi-instance learning (MIL; BIBREF11 , BIBREF11 ) which automatically discovers correspondences, namely alignments, between data and text pairs. These alignments are then used to modify the generation function during training. We experiment with two frameworks that allow to incorporate alignment information, namely multi-task learning (MTL; BIBREF12 , BIBREF12 ) and reinforcement learning (RL; BIBREF13 , BIBREF13 ). In both cases we define novel objective functions using the learnt alignments. Experimental results using automatic and human-based evaluation show that models trained with content-specific objectives improve upon vanilla encoder-decoder architectures which rely solely on soft attention. The remainder of this paper is organised as follows. We discuss related work in Section "Related Work" and describe the MIL-based content selection approach in Section "Bidirectional Content Selection" . We explain how the generator is trained in Section "Generator Training" and present evaluation experiments in Section "Experimental Setup" . Section "Conclusions" concludes the paper.

Question: How many domains of ontologies do they gather data from?
Answer: 5 domains: software, stuff, african wildlife, healthcare, datatypes