Hypothesis Only Baselines in Natural Language Inference

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.Comment: To be presented at EMNLP 2018. 15 page

Understanding Equitable Assessment: How Preservice Teachers Make Meaning of DisAbility

Disproportionality of historically marginalized populations in special education continues to be a critical concern. The identification of students with disabilities is reliant on valid and reliable assessment that is free of bias. The extent to which this is possible given measurement constraints and an increasingly diverse student population is unclear. How teachers are trained to design, select, administer, score, and interpret assessment data related to the identification of students with disabilities is vastly under-researched considering the significant implications of assessment practices. In this study, six special education preservice teachers engaged in an assessment methods course during their second semester of an initial certification program. This study focuses on shifts in preservice teacher understanding and the associated learning experiences in the course. Findings from this study have the potential to inform general and special education teacher preparation coursework

The brain is a prediction machine that cares about good and bad - Any implications for neuropragmatics?

Experimental pragmatics asks how people construct contextualized meaning in communication. So what does it mean for this field to add neuroas a prefix to its name? After analyzing the options for any subfield of cognitive science, I argue that neuropragmatics can and occasionally should go beyond the instrumental use of EEG or fMRI and beyond mapping classic theoretical distinctions onto Brodmann areas. In particular, if experimental pragmatics ‘goes neuro’, it should take into account that the brain evolved as a control system that helps its bearer negotiate a highly complex, rapidly changing and often not so friendly environment. In this context, the ability to predict current unknowns, and to rapidly tell good from bad, are essential ingredients of processing. Using insights from non-linguistic areas of cognitive neuroscience as well as from EEG research on utterance comprehension, I argue that for a balanced development of experimental pragmatics, these two characteristics of the brain cannot be ignored

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

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

Ethnographic Causality

This book explores the problem of causal inference when a sufficient number of comparative cases cannot be found, which would permit the application of frequency based models formulated in terms of explanatory causal generalizations