scholarly journals Ranking Algorithms for Word Ordering in Surface Realization

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 337
Author(s):  
Alessandro Mazzei ◽  
Mattia Cerrato ◽  
Roberto Esposito ◽  
Valerio Basile
Keyword(s):  
Natural Language ◽  
Learning To Rank ◽  
Natural Language Generation ◽  
Surface Form ◽  
Natural Languages ◽  
Language Generation ◽  
Output Surface ◽  
Ranking Algorithms ◽  
Arbitrary Tree ◽  
Surface Realization

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.

scholarly journals Aggregation with Recombination Patterns

Triangle ◽  
2018 ◽  
pp. 89
Author(s):  
María Dolores Jiménez López
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Natural Languages ◽  
Language Generation ◽  
Complex Sentences ◽  
Aggregation Processes

In this paper, we show the commonalities between aggregation processes in Natural Language Generation and recombination patterns, a framework introduced recently as a way of generating complex sentences in natural languages using very simple recombination –and therefore biological– rules. By showing similarities between these two mechanisms, we suggest the possibility of carrying out aggregation by means of recombination patterns. We also refer to the possibility of using such a biological-motivated framework in the design of efficient and simple natural language generation devices.

scholarly journals The Rare Word Issue in Natural Language Generation: A Character-Based Solution

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Giovanni Bonetta ◽  
Marco Roberti ◽  
Rossella Cancelliere ◽  
Patrick Gallinari
Keyword(s):  
Natural Language ◽  
Error Probability ◽  
Essential Feature ◽  
Proper Names ◽  
Neural Model ◽  
Natural Language Generation ◽  
Training Phase ◽  
Tabular Data ◽  
Rare Word ◽  
Language Generation

In this paper, we analyze the problem of generating fluent English utterances from tabular data, focusing on the development of a sequence-to-sequence neural model which shows two major features: the ability to read and generate character-wise, and the ability to switch between generating and copying characters from the input: an essential feature when inputs contain rare words like proper names, telephone numbers, or foreign words. Working with characters instead of words is a challenge that can bring problems such as increasing the difficulty of the training phase and a bigger error probability during inference. Nevertheless, our work shows that these issues can be solved and efforts are repaid by the creation of a fully end-to-end system, whose inputs and outputs are not constrained to be part of a predefined vocabulary, like in word-based models. Furthermore, our copying technique is integrated with an innovative shift mechanism, which enhances the ability to produce outputs directly from inputs. We assess performance on the E2E dataset, the benchmark used for the E2E NLG challenge, and on a modified version of it, created to highlight the rare word copying capabilities of our model. The results demonstrate clear improvements over the baseline and promising performance compared to recent techniques in the literature.

ProBot – A Procedure Chatbot for Digital Procedural Adherence

2020 ◽  
Vol 64 (1) ◽  
pp. 224-228
Author(s):  
Nilesh Ade ◽  
Noor Quddus ◽  
Trent Parker ◽  
S.Camille Peres
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language ◽  
Industry 4.0 ◽  
Natural Language Generation ◽  
Conversational Agent ◽  
Safe Distance ◽  
Process Industries ◽  
Language Generation ◽  
Plastic Pellets

One of the major implications of Industry 4.0 will be the application of digital procedures in process industries. Digital procedures are procedures that are accessed through a smart gadget such as a tablet or a phone. However, like paper-based procedures their usability is limited by their access. The issue of accessibility is magnified in tasks such as loading a hopper car with plastic pellets wherein the operators typically place the procedure at a safe distance from the worksite. This drawback can be tackled in the case of digital procedures using artificial intelligence-based voice enabled conversational agent (chatbot). As a part of this study, we have developed a chatbot for assisting digital procedure adherence. The chatbot is trained using the possible set of queries from the operator and text from the digital procedures through deep learning and provides responses using natural language generation. The testing of the chatbot is performed using a simulated conversation with an operator performing the task of loading a hopper car.

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