124 citations · 161 across the 3 of their papers we have counts for
6 papers · 1 filter
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction
Yue Yang, Artemis Panagopoulou, Marianna Apidianaki +2
Neural language models encode rich knowledge about entities and their relationships which can be extracted from their representations using probing. Common properties of nouns (e.g…
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Christopher Clark, Mark Yatskar, Luke Zettlemoyer
State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment mod…
Gender Bias in Contextualized Word Embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar +3
In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) trainin…
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
Mark Yatskar
We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (…
QuAC : Question Answering in Context
Eunsol Choi, He He, Mohit Iyyer +5
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1)…
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar +2
We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people…