Reviewing Natural Language Processing Research

International audienceThis tutorial will cover the goals, processes, and evaluation of reviewing research in natural language processing. As has been pointed out for years by leading figures in our community (Web-ber, 2007), researchers in the ACL community face a heavy-and growing-reviewing burden. Initiatives to lower this burden have been discussed at the recent ACL general assembly in Florence (ACL 2019) 1. Simultaneously, notable "false negatives"-rejection by our conferences of work that was later shown to be tremendously important after acceptance by other conferences (Church, 2005)-has raised awareness of the fact that our reviewing practices leave something to be desired.. . and we do not often talk about "false positives" with respect to conference papers, but conversations in the hallways at *ACL meetings suggest that we have a publication bias towards papers that report high performance, with perhaps not much else of interest in them (Manning, 2015). It need not be this way. There is good reason to think that reviewing is a learnable (and teachable)

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Construction contract risk identification based on knowledge-augmented language model

Contract review is an essential step in construction projects to prevent potential losses. However, the current methods for reviewing construction contracts lack effectiveness and reliability, leading to time-consuming and error-prone processes. While large language models (LLMs) have shown promise in revolutionizing natural language processing (NLP) tasks, they struggle with domain-specific knowledge and addressing specialized issues. This paper presents a novel approach that leverages LLMs with construction contract knowledge to emulate the process of contract review by human experts. Our tuning-free approach incorporates construction contract domain knowledge to enhance language models for identifying construction contract risks. The use of a natural language when building the domain knowledge base facilitates practical implementation. We evaluated our method on real construction contracts and achieved solid performance. Additionally, we investigated how large language models employ logical thinking during the task and provide insights and recommendations for future research

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

Sensemaking and lens-shaping : Identifying citizen contributions to foresight through comparative topic modelling

As foresight activities continue to increase across multiple arenas and types of organizations, the need to develop effective modes of reviewing future-oriented information against long-term goals and policies becomes more pressing. The activities of institutional sensemaking are vital in constructing potential and desired futures, but remain sensitive to organizational culture and ethos, thus raising concerns about whose futures are being constructed. In viewing foresight studies as a critical component in such sensemaking, this research investigates a method of textual analysis that deploys natural language processing algorithms (NLP). In this research, we introduce and apply the methodology of topic modelling for conducting a comparative analysis to explore how citizen-derived foresight differs from other institutional foresight. Finally we present pros-pects for further employing NLP for strategic foresight and futures studies.Peer reviewe

Augmented Language Models: a Survey

This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics, or learn to do so from demonstrations. While adhering to a standard missing tokens prediction objective, such augmented LMs can use various, possibly non-parametric external modules to expand their context processing ability, thus departing from the pure language modeling paradigm. We therefore refer to them as Augmented Language Models (ALMs). The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks. In this work, after reviewing current advance in ALMs, we conclude that this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues

INVESTIGATING INTELLECTUAL DIVERSITY: A CRITICAL EXAMINATION OF ACADEMIC PUBLISHING PRACTICES AND THEIR EFFECTS ON WILDLIFE CONSERVATION

Academic publishing processes and standards play a fundamental role in communicating, reviewing, and expanding scientific knowledge in wildlife conservation. However, various publishing biases privilege some research perspectives and worldviews while limiting others. These biases directly impact intellectual diversity, or differences in ontology, axiology, and epistemology. This study aims to quantify intellectual diversity in the field of wildlife conservation and identify how publishing biases affect knowledge available to researchers and decision-makers worldwide. The study employed a sample of 50,000 articles published between 2018 and 2022, collected from the Web of Science database. To analyze the vast amount of article records, natural language processing techniques, including topic modeling, were applied to article abstracts. This enabled the identification of global differences in prevalent topics, theories, and methods in wildlife conservation research. By connecting these trends with researcher social diversity, the study seeks to understand the influence of diverse perspectives on research design and knowledge production. Additionally, an intellectual diversity survey was sent to a randomized sample of international and domestic authors to gather data on differences in axiology and epistemology as well as various publishing culture dynamics. Results reveal the existence of several biases in publishing culture, aligning with previous research. Moreover, language bias emerged as a primary concern, with researchers who did not speak English as a first language experiencing publishing biases most strongly. Differences in epistemological and axiological beliefs were also observed between demographic groups and connected to current work in value orientations and knowledge dimensions. Topic modeling revealed strong geographic differences in topics of study, and natural language processing demonstrated differences in research design. The study contributes to the ongoing discourse on the importance of diversity in wildlife conservation, management, and policy. By addressing biases and fostering intellectual diversity, researchers can effectively tackle complex global challenges. The findings of this research will inform future efforts to explore intellectual diversity and feasible approaches to reducing inherent barriers and biases in academic publishing

Retrospective Analysis and Prediction: Artificial Intelligence and Its Applications in Libraries

The application of Artificial Intelligence (AI) has brought significant innovation to fundamental science and research in recent years. This paper briefly reviews and analyzes the findings of research and development of AI technologies such as expert systems, natural language processing, pattern recognition, robotics and machine learning in the fields of library such as information retrieval, reference service, cataloging, classification, acquisitions, circulation and automation. By reviewing and analyzing research papers published on respected academic journals, studying the examples and practical cases of the latest AI applications in industry, this study finds that current AI applications in the field of library are still in the narrow AI or weak AI called machine learning phase. However, the emerging technologies such as Biometrics Identification, Robotics, Deep Learning and Neural Networks have been used by libraries and library automation. In particular, this paper looks into the possibilities of the application of general AI or strong AI into the field of Information Retrieval