Abstract
In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the broader context of surface realization. The major contributions of this paper are: (i) the design of three deep neural architectures implementing pointwise, pairwise, and listwise approaches for ranking; (ii) the testing of these neural architectures on a surface realization benchmark in five natural languages belonging to different typological families. The results of our experiments show promising results, in particular highlighting the performance of the pairwise approach, paving the way for a more transparent surface realization from arbitrary tree- and graph-like structures.
Highlights
Natural language generation (NLG) is the computational process of producing natural language from a formal structure representing some information
We first discuss the main ranking algorithms related to our approach (Section 2.1), we report the main approaches to word ordering from the NLG literature (Section 2.2)
The yellow lines report the best scores obtained for each specific language in the various editions of Surface Realization Shared Task [10–12] (SRST) [10,12], in particular: ADAPT (20b) is bidirectional recurrent neural network with long short term memory augmented the WikiText 103 and CNN stories corpora [12]; IMS (20a), (20b) is a bidirectional Tree-LSTM encoder architecture word ordering as a traveling salesman problem, that uses a biaffine attention model to calculate the bigram scores [40]; Tilburg-MT18 is a word ordering algorithm based on the neural machine translation paradigm [10]
Summary
Natural language generation (NLG) is the computational process of producing natural language from a formal structure representing some information. NLG systems exist that take input structures ranging from numeric tables to syntactic trees, to abstract representations of meaning. Just as the input to NLG, the output of NLU may vary in terms of complexity and scope of the analysis, but in general it is a data structure containing several types of semantic information. The classical Montague semantic analysis produces predicate–argument recursive structures expressing subject–predicate–object relations [1]. Another example, one that uses statistics for merging substructures, is abstract meaning representation (AMR), where the final structure is a directed acyclic graph [2]. Modern commercial dialog systems, such as Google Dialogflow, often produce simpler, non-recursive semantics structures (https://cloud.google.com/dialogflow/es/docs/basics, accessed on 17 August 2021)
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