Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous studies heavily depend on a great deal of complex expert-defined knowledge such as alignment rules and assessment metrics, thus suffer from the problems of low accuracy and poor adaptability when applied in open-domain scenarios. To address the above issues, this paper makes the first attempt that uses deep learning to perform PCC. Specifically, we proposed TraceWalk, using semantic information of process graphs to learn latent node representations, and integrates it into a convolutional neural network (CNN) based model called TraceNet to predict consistencies. The theoretical proof formally provides the PCC's lower limit and experimental results demonstrate that our approach performs more accurately than state-of-the-art baselines.
Along this line, alignment-based PCC methods designed various alignment rules between process graphs and texts. We summarize and show their used procedures in Table . @cite_2 (language-analysis based) set out to create an action-sentence correspondence relation between an action of a process graph and a sentence of a process text through linguistic analysis, similarity computation and best-first searching. @cite_14 (language-analysis based) extended previous work in order to detect missing actions and conflicting orders. Thus can detect inconsistencies in a much more fine-granular manner. @cite_17 (manual-feature based) extended the linguistic analysis and encoded the problem of computing an alignment as the resolution of an integer linear programming problem. @cite_3 (manual-feature based) extracted features that correspond to important process-related information and used so-called predictors to detect if a provided model-text pair is likely to contain inconsistencies. These methods require language analysis, manual feature extraction and sentence similarity computation etc. They heavily depend on existing language analysis tools, and always cause the weak generalization and adaptability.
@cite_14 @cite_17 @cite_3 @cite_2
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A range of Natural Language Processing tasks involve making judgments about the semantic relatedness of a pair of sentences, such as Recognizing Textual Entailment (RTE) and answer selection for Question Answering (QA). A key challenge that these tasks face in common is the lack of explicit alignment annotation between a sentence pair. We capture the alignment by using a novel probabilistic model that models tree-edit operations on dependency parse trees. Unlike previous tree-edit models which require a separate alignment-finding phase and resort to ad-hoc distance metrics, our method treats alignments as structured latent variables, and offers a principled framework for incorporating complex linguistic features. We demonstrate the robustness of our model by conducting experiments for RTE and QA, and show that our model performs competitively on both tasks with the same set of general features. This paper addresses the task of aligning a database with a corresponding text. The goal is to link individual database entries with sentences that verbalize the same information. By providing explicit semantics-to-text links, these alignments can aid the training of natural language generation and information extraction systems. Beyond these pragmatic benefits, the alignment problem is appealing from a modeling perspective: the mappings between database entries and text sentences exhibit rich structural dependencies, unique to this task. Thus, the key challenge is to make use of as many global dependencies as possible without sacrificing tractability. To this end, we cast text-database alignment as a structured multilabel classification task where each sentence is labeled with a subset of matching database entries. In contrast to existing multilabel classifiers, our approach operates over arbitrary global features of inputs and proposed labels. We compare our model with a baseline classifier that makes locally optimal decisions. Our results show that the proposed model yields a 15 relative reduction in error, and compares favorably with human performance. We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects. Given a sequence of natural language instructions and an unaligned video recording, we simultaneously align each instruction to its corresponding video segment, and also align nouns in each instruction to their corresponding objects in video. While existing grounded language acquisition algorithms rely on pre-aligned supervised data (each sentence paired with corresponding image frame or video segment), our algorithm aims to automatically infer the alignment from the temporal structure of the video and parallel text instructions. We propose two generative models that are closely related to the HMM and IBM 1 word alignment models used in statistical machine translation. We evaluate our algorithm on videos of biological experiments performed in wetlabs, and demonstrate its capability of aligning video segments to text instructions and matching video objects to nouns in the absence of any direct supervision. With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.