RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans

The text editing tasks, including sentence fusion, sentence splitting and rephrasing, text simplification, and Grammatical Error Correction (GEC), share a common trait of dealing with highly similar input and output sequences. This area of research lies at the intersection of two well-established fields: (i) fully autoregressive sequence-to-sequence approaches commonly used in tasks like Neural Machine Translation (NMT) and (ii) sequence tagging techniques commonly used to address tasks such as Part-of-speech tagging, Named-entity recognition (NER), and similar. In the pursuit of a balanced architecture, researchers have come up with numerous imaginative and unconventional solutions, which we're discussing in the Related Works section. Our approach to addressing text editing tasks is called RedPenNet and is aimed at reducing architectural and parametric redundancies presented in specific Sequence-To-Edits models, preserving their semi-autoregressive advantages. Our models achieve $F_{0.5}$ scores of 77.60 on the BEA-2019 (test), which can be considered as state-of-the-art the only exception for system combination and 67.71 on the UAGEC+Fluency (test) benchmarks. This research is being conducted in the context of the UNLP 2023 workshop, where it was presented as a paper as a paper for the Shared Task in Grammatical Error Correction (GEC) for Ukrainian. This study aims to apply the RedPenNet approach to address the GEC problem in the Ukrainian language

Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD

Formal approaches to number in Slavic and beyond (Volume 5)

The goal of this collective monograph is to explore the relationship between the cognitive notion of number and various grammatical devices expressing this concept in natural language with a special focus on Slavic. The book aims at investigating different morphosyntactic and semantic categories including plurality and number-marking, individuation and countability, cumulativity, distributivity and collectivity, numerals, numeral modifiers and classifiers, as well as other quantifiers. It gathers 19 contributions tackling the main themes from different theoretical and methodological perspectives in order to contribute to our understanding of cross-linguistic patterns both in Slavic and non-Slavic languages

Why and How to Extract Conditional Statements From Natural Language Requirements

Functional requirements often describe system behavior by relating events to each other, e.g. "If the system detects an error (e_1), an error message shall be shown (e_2)". Such conditionals consist of two parts: the antecedent (see e_1) and the consequent (e_2), which convey strong, semantic information about the intended behavior of a system. Automatically extracting conditionals from texts enables several analytical disciplines and is already used for information retrieval and question answering. We found that automated conditional extraction can also provide added value to Requirements Engineering (RE) by facilitating the automatic derivation of acceptance tests from requirements. However, the potential of extracting conditionals has not yet been leveraged for RE. We are convinced that this has two principal reasons: 1) The extent, form, and complexity of conditional statements in RE artifacts is not well understood. We do not know how conditionals are formulated and logically interpreted by RE practitioners. This hinders the development of suitable approaches for extracting conditionals from RE artifacts. 2) Existing methods fail to extract conditionals from Unrestricted Natural Language (NL) in fine-grained form. That is, they do not consider the combinatorics between antecedents and consequents. They also do not allow to split them into more fine-granular text fragments (e.g., variable and condition), rendering the extracted conditionals unsuitable for RE downstream tasks such as test case derivation. This thesis contributes to both areas. In Part I, we present empirical results on the prevalence and logical interpretation of conditionals in RE artifacts. Our case study corroborates that conditionals are widely used in both traditional and agile requirements such as acceptance criteria. We found that conditionals in requirements mainly occur in explicit, marked form and may include up to three antecedents and two consequents. Hence, the extraction approach needs to understand conjunctions, disjunctions, and negations to fully capture the relation between antecedents and consequents. We also found that conditionals are a source of ambiguity and there is not just one way to interpret them formally. This affects any automated analysis that builds upon formalized requirements (e.g., inconsistency checking) and may also influence guidelines for writing requirements. Part II presents our tool-supported approach CiRA capable of detecting conditionals in NL requirements and extracting them in fine-grained form. For the detection, CiRA uses syntactically enriched BERT embeddings combined with a softmax classifier and outperforms existing methods (macro-F_1: 82%). Our experiments show that a sigmoid classifier built on RoBERTa embeddings is best suited to extract conditionals in fine-grained form (macro-F_1: 86%). We disclose our code, data sets, and trained models to facilitate replication. CiRA is available at http://www.cira.bth.se/demo/. In Part III, we highlight how the extraction of conditionals from requirements can help to create acceptance tests automatically. First, we motivate this use case in an empirical study and demonstrate that the lack of adequate acceptance tests is one of the major problems in agile testing. Second, we show how extracted conditionals can be mapped to a Cause-Effect-Graph from which test cases can be derived automatically. We demonstrate the feasibility of our approach in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8% can be generated automatically. Furthermore, our approach discovered 80 relevant test cases that were missed in manual test case design. At the end of this thesis, the reader will have an understanding of (1) the notion of conditionals in RE artifacts, (2) how to extract them in fine-grained form, and (3) the added value that the extraction of conditionals can provide to RE

Transformer Neural Networks for Automated Story Generation

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given input, but an ability to be creative, fluent and, coherent within a content. One of these tasks is automated story generation which has been an open research area from the early days of artificial intelligence. This study investigates whether the transformer network can outperform state-of-the-art model for automated story generation. A large dataset gathered from Reddit’s WRITING PROMPTS sub forum and processed by the transformer network in order to compare the perplexity and two human evaluation metrics on transformer network and the state-of-the-art model. It was found that the transformer network cannot outperform the state-of-art model and even though it generated viable and novel stories it didn’t pay much attention to the prompts of the generated stories. Also, the results implied that there should be a better automated evaluation metric in order to assess the performance of story generation models