Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities

Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks

State-of-the-art models can perform well in controlled environments, but they often struggle when presented with out-of-distribution (OOD) examples, making OOD detection a critical component of NLP systems. In this paper, we focus on highlighting the limitations of existing approaches to OOD detection in NLP. Specifically, we evaluated eight OOD detection methods that are easily integrable into existing NLP systems and require no additional OOD data or model modifications. One of our contributions is providing a well-structured research environment that allows for full reproducibility of the results. Additionally, our analysis shows that existing OOD detection methods for NLP tasks are not yet sufficiently sensitive to capture all samples characterized by various types of distributional shifts. Particularly challenging testing scenarios arise in cases of background shift and randomly shuffled word order within in domain texts. This highlights the need for future work to develop more effective OOD detection approaches for the NLP problems, and our work provides a well-defined foundation for further research in this area.Comment: 11 pages, 3 figures, Association for Computational Linguistic

Hybrid robust deep and shallow semantic processing for creativity support in document production

The research performed in the DeepThought project (http://www.project-deepthought.net) aims at demonstrating the potential of deep linguistic processing if added to existing shallow methods that ensure robustness. Classical information retrieval is extended by high precision concept indexing and relation detection. We use this approach to demonstrate the feasibility of three ambitious applications, one of which is a tool for creativity support in document production and collective brainstorming. This application is described in detail in this paper. Common to all three applications, and the basis for their development is a platform for integrated linguistic processing. This platform is based on a generic software architecture that combines multiple NLP components and on robust minimal recursive semantics (RMRS) as a uniform representation language

Not the Whole Story of the National Literacy Strategy: A Response to Dominic Wyse

There is evidence that the National Literacy Strategy has led to a sustained increase in literacy attainment, especially in reading, although recent international comparisons also suggest some additional issues regarding pupil performance in England. The relative success of the NLS may at least partly lie in the policy application of several complementary areas of educational research, a suggestion disputed by Dominic Wyse (2003). However, his critical commentary is marred by important omissions, particularly of reference to debates about the teaching of reading and to the statutory status of the National Curriculum for English. His alternative suggestions on the use of ‘child development’ evidence lack methodological detail and are only partly formulated

An Overview on Language Models: Recent Developments and Outlook

Language modeling studies the probability distributions over strings of texts. It is one of the most fundamental tasks in natural language processing (NLP). It has been widely used in text generation, speech recognition, machine translation, etc. Conventional language models (CLMs) aim to predict the probability of linguistic sequences in a causal manner. In contrast, pre-trained language models (PLMs) cover broader concepts and can be used in both causal sequential modeling and fine-tuning for downstream applications. PLMs have their own training paradigms (usually self-supervised) and serve as foundation models in modern NLP systems. This overview paper provides an introduction to both CLMs and PLMs from five aspects, i.e., linguistic units, structures, training methods, evaluation methods, and applications. Furthermore, we discuss the relationship between CLMs and PLMs and shed light on the future directions of language modeling in the pre-trained era