Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness

Modeling Event Plausibility with Consistent Conceptual Abstraction

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models -- most recently pre-trained, Transformer language models -- have demonstrated improvements in modeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that "a person breathing" is plausible while "a dentist breathing" is not, for example. We find this inconsistency pe…

How Lexical Gold Standards Have Effects On The Usefulness Of Text Analysis Tools For Digital Scholarship

This paper describes how the current lexical similarity and analogy gold standards are built to conform to certain ideas about what the models they are designed to evaluate are used for. Topical relevance has always been the most important target notion for information access tools and related language technology technologies, and while this has proven a useful starting point for much of what information technology is used for, it does not always align well with other uses to which technologies are being put, most notably use cases from digital scholarship in the humanities or social sciences.…

Simulating Lexical Semantic Change from Sense-Annotated Data

We present a novel procedure to simulate lexical semantic change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical semantic change detection models. The induced dataset represents a stronger correspondence to empirically observed lexical semantic change than previous synthetic datasets, because it exploits the intimate relationship between synchronic polysemy and diachronic change. We publish the data and provide the first large-scale evaluation gold standard for LSC detection models.

分析子词分词的认知合理性

Analyzing Cognitive Plausibility of Subword Tokenization

提出评估子词分词认知合理性的新范式，通过分析分词器输出与人类词汇决策任务表现的相关性，发现UnigramLM算法的认知合理性及派生语素覆盖较差。

StyLEx：使用人类词汇标注解释风格

StyLEx: Explaining Style Using Human Lexical Annotations

StyLEx通过联合学习风格分类与人类标注的风格特征解释，在不降低预测性能的前提下生成更符合人类认知、可理解性更强的词汇级解释。

Lexico-acoustic Neural-based Models for Dialog Act Classification

Recent works have proposed neural models for dialog act classification in spoken dialogs. However, they have not explored the role and the usefulness of acoustic information. We propose a neural model that processes both lexical and acoustic features for classification. Our results on two benchmark datasets reveal that acoustic features are helpful in improving the overall accuracy. Finally, a deeper analysis shows that acoustic features are valuable in three cases: when a dialog act has sufficient data, when lexical information is limited and when strong lexical cues are not present.