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Transfer Learning Between Related Tasks Using Expected Label Proportions
Matan Ben Noach, Yoav GoldbergEMNLP • 2019 Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained…Question Answering is a Format; When is it Useful?
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon MinarXiv • 2019 Recent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to image and video understanding. With this expansion, there are many differing…Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Mor Geva, Yoav Goldberg, Jonathan BerantarXiv • 2019 Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a…MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
Alon Talmor, Jonathan BerantACL • 2019 A large number of reading comprehension (RC) datasets has been created recently, but little analysis has been done on whether they generalize to one another, and the extent to which existing datasets can be leveraged for improving performance on new ones. In…Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Ben Bogin, Jonathan Berant, Matt GardnerACL • 2019 Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In SPIDER, a recently-released text-to-SQL dataset, new and…Aligning Vector-spaces with Noisy Supervised Lexicons
Noa Yehezkel, Jacob Goldberger, Yoav GoldbergNAACL • 2019 The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the…CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan BerantNAACL • 2019 When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background…DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion
Mor Geva, Eric Malmi, Idan Szpektor, Jonathan BerantNAACL • 2019 Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically…Evaluating Text GANs as Language Models
Guy Tevet, Gavriel Habib, Vered Shwartz, Jonathan BerantNAACL • 2019 Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle for understanding the potential of GANs for text…Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Hila Gonen, Yoav GoldbergNAACL • 2019 Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious…