Exploring the Landscape of Natural Language Processing Research (2023)

Abstract: As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

## introduction natural language is a fundamental aspect of human communication and inherent to human utterances and information sharing. accordingly, most human-generated digital data are composed in natural language. given the ever-increasing amount and importance of digital data, it is not surprising that computational linguists have started developing ideas on enabling machines to understand, generate, and process natural language since the 1950s @xcite . more recently, the introduction of the transformer model @xcite and pretrained language models @xcite have sparked increasing interest in natural language processing (nlp). submissions on various nlp topics and applications are being published in a growing number of journals and conferences, such as tacl, acl, and emnlp, 1 code available: https://github.com/sebischair/exploringnlp-research as well as in several smaller workshops that focus on specific areas. thereby, the acl anthology foot_0 as a repository for publications from many major nlp journals, conferences, and workshops emerges as an important tool for researchers. as of january 2023, it provides access to over 80,000 articles published since 1952. figure 1 shows the distribution of publications in the acl anthology over the 50-year observation period. @xcite accompanying the increase in publications, there has also been a growth in the number of different fields of study (fos) that have been researched within the nlp domain. fos are academic disciplines and concepts that usually consist of (but are not limited to) tasks or techniques @xcite . given the rapid developments in nlp research, obtaining an overview of the domain and maintaining it is difficult. as such, collecting insights, consolidating existing results, and presenting a structured overview of the field is important. however, to the best of our knowledge, no stud- relation extraction machine translation speech recognition text style transfer code generation ies exist yet that offer an overview of the entire landscape of nlp research. to bridge this gap, we performed a comprehensive study to analyze all research performed in this area by classifying established topics, identifying trends, and outlining areas for future research. our three main contributions are as follows: our study highlights the development and current state of nlp research. although we cannot fully cover all relevant work on this topic, we aim to provide a representative overview that can serve as a starting point for both nlp scholars and practitioners. in addition, our analysis can assist the research community in bridging existing gaps and exploring various fos in nlp. ## related work related literature that considers various different fos in nlp is relatively scarce. most studies focus only on a particular fos or sub-field of nlp research. for example, related studies focus on knowledge graphs in nlp @xcite , explainability in nlp @xcite , ethics and biases in nlp @xcite , question answering @xcite , or knowledge representations in language models @xcite . studies that analyze nlp research based on the entire acl anthology focus on citation analyses @xcite or visualizations of venues, authors, and n-grams and keywords extracted from publications @xcite . @xcite apply topic modeling to identify different epochs in the acl's history. various books categorize different fos in nlp, focusing on detailed explanations for each of these categories @xcite @xcite @xcite . ## research questions the goal of our study is an extensive analysis of research performed in nlp by classifying established topics, identifying trends, and outlining areas for future research. these objectives are reflected in our research questions (rqs) presented as follows: rq1: what are the different fos investigated in nlp research? although most fos in nlp are well-known and defined, there currently exists no commonly used taxonomy or categorization scheme that attempts to collect and structure these fos in a consistent and understandable format. therefore, getting an overview of the entire field of nlp research is difficult, especially for students and early career researchers. while there are lists of nlp topics in conferences and textbooks, they tend to vary considerably and are often either too broad or too specialized. to classify and analyze developments in nlp, we need a taxonomy that encompasses a wide range of different fos in nlp. although this taxonomy may not include all possible nlp concepts, it needs to cover a wide range of the most popu-lar fos, whereby missing fos may be considered as subtopics of the included fos. this taxonomy serves as an overarching classification scheme in which nlp publications can be classified according to at least one of the included fos, even if they do not directly address one of the fos, but only subtopics thereof. ## classification & analysis in this section, we report the approaches and results of the data classification and analysis. it is structured according to the formulated rqs. ## discussion the observations of our comprehensive study reveal several insights that we can situate to related work. since the first publications in 1952, researchers have paid increasing attention to the field of nlp, particularly after the introduction of word2vec @xcite and accelerated by bert @xcite . this observed growth in research interest is in line with the study of mohammad (2020b). historically, machine translation was one of the first research fields in nlp @xcite , which continues to be popular and steadily growing nowadays. however, recent advances in language model training have sparked increasing research efforts in this field, as shown in figure 3 and figure 4 . since scaling up language models significantly enhance performance on downstream tasks @xcite @xcite , researchers continue to introduce increasingly larger language models @xcite . however, training and using these large language models involves significant challenges, including computational costs @xcite , environmental issues @xcite , and ethical considerations @xcite . as a result, a recent increase in research efforts has been noted to render language models and nlp more responsible & trustworthy in general, as shown in figure 4 . additionally, recent advances aim to train large-scale multimodal language models capable of understanding and generating natural language text and performing all types of downstream tasks while interacting with humans through natural language input prompts @xcite . from our observations in figure 4 , we again find support for this trend in nlp literature for multimodality, text generation, and natural language interfaces. although language models have achieved remarkable success on various nlp tasks, their inability to reason is often seen as a limitation that cannot be overcome by increasing the model size alone @xcite @xcite . although reasoning capabilities are a crucial prerequisite for the reliability of language models, this field is still relatively less researched and receives negligible attention. while figure 4 exhibits high growth rates for knowledge graph reasoning and numerical reasoning in particular, research related to reasoning is still rather underrepresented compared to the more popular fos. ## conclusion recent years have witnessed an increasing prominence of nlp research. to summarize recent developments and provide an overview of this research area, we defined a taxonomy of fos in nlp and analyzed recent research developments. our findings show that a large number of fos have been studied, including trending fields such as multimodality, responsible & trustworthy nlp, and natural language interfaces. while recent developments are largely a result of recent advances in language models, we have noted a lack of research pertaining to teaching these language models to reason and thereby afford more reliable predictions.