Living Machines: A study of atypical animacy

This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use

Decompositional Semantics for Events, Participants, and Scripts in Text

This thesis presents a sequence of practical and conceptual developments in decompositional meaning representations for events, participants, and scripts in text under the framework of Universal Decompositional Semantics (UDS) (White et al., 2016a). Part I of the thesis focuses on the semantic representation of individual events and their participants. Chapter 3 examines the feasibility of deriving semantic representations of events from dependency syntax; we demonstrate that predicate- argument structure may be extracted from syntax, but other desirable semantic attributes are not directly discernible. Accordingly, we present in Chapters 4 and 5 state of the art models for predicting these semantic attributes from text. Chapter 4 presents a model for predicting semantic proto-role labels (SPRL), attributes of participants in events based on Dowty’s seminal theory of thematic proto-roles (Dowty, 1991). In Chapter 5 we present a model of event factuality prediction (EFP), the task of determining whether an event mentioned in text happened (according to the meaning of the text). Both chapters include extensive experiments on multi-task learning for improving performance on each semantic prediction task. Taken together, Chapters 3, 4, and 5 represent the development of individual components of a UDS parsing pipeline. In Part II of the thesis, we shift to modeling sequences of events, or scripts (Schank and Abelson, 1977). Chapter 7 presents a case study in script induction using a collection of restaurant narratives from an online blog to learn the canonical “Restaurant Script.” In Chapter 8, we introduce a simple discriminative neural model for script induction based on narrative chains (Chambers and Jurafsky, 2008) that outperforms prior methods. Because much existing work on narrative chains employs semantically impoverished representations of events, Chapter 9 draws on the contributions of Part I to learn narrative chains with semantically rich, decompositional event representations. Finally, in Chapter 10, we observe that corpus based approaches to script induction resemble the task of language modeling. We explore the broader question of the relationship between language modeling and acquisition of common-sense knowledge, and introduce an approach that combines language modeling and light human supervision to construct datasets for common-sense inference

What’s the Meaning of Superhuman Performance in Today’s NLU?

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases. This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved. In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating. We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks.</p

Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models

Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort. While performing annotation work on crowdsourcing platforms, we hypothesize that such heuristic use among annotators cascades on to data quality and model robustness. In this work, we study cognitive heuristic use in the context of annotating multiple-choice reading comprehension datasets. We propose tracking annotator heuristic traces, where we tangibly measure low-effort annotation strategies that could indicate usage of various cognitive heuristics. We find evidence that annotators might be using multiple such heuristics, based on correlations with a battery of psychological tests. Importantly, heuristic use among annotators determines data quality along several dimensions: (1) known biased models, such as partial input models, more easily solve examples authored by annotators that rate highly on heuristic use, (2) models trained on annotators scoring highly on heuristic use don't generalize as well, and (3) heuristic-seeking annotators tend to create qualitatively less challenging examples. Our findings suggest that tracking heuristic usage among annotators can potentially help with collecting challenging datasets and diagnosing model biases.Comment: EMNLP 202

IMPACT OF DATA COLLECTION ON ML MODELS: ANALYZING DIFFERENCES OF BIASES BETWEEN LOW- VS. HIGH-SKILLED ANNOTATORS

Labeled data is crucial for the success of machine learning-based artificial intelligence. However, companies often face a choice between collecting few annotations from high- or low-skilled annotators, possibly exhibiting different biases. This study investigates differences in biases between datasets labeled by said annotator groups and their impact on machine learning models. Therefore, we created high- and low-skilled annotated datasets measured the contained biases through entropy and trained different machine learning models to examine bias inheritance effects. Our findings on text sentiment annotations show both groups exhibit a considerable amount of bias in their annotations, although there is a significant difference regarding the error types commonly encountered. Models trained on biased annotations produce significantly different predictions, indicating bias propagation and tend to make more extreme errors than humans. As partial mitigation, we propose and show the efficiency of a hybrid approach where data is labeled by low-skilled and high-skilled workers