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08/01/2023
The Bias Amplification Paradox in Text-to-Image Generation
Bias amplification is a phenomenon in which models increase imbalances p...
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06/24/2023
Estimating the Causal Effect of Early ArXiving on Paper Acceptance
What is the effect of releasing a preprint of a paper before it is submi...
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05/26/2023
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Few-shot fine-tuning and in-context learning are two alternative strateg...
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03/07/2023
At Your Fingertips: Extracting Piano Fingering Instructions from Videos
Piano fingering – knowing which finger to use to play each note in a mus...
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10/23/2022
Lexical Generalization Improves with Larger Models and Longer Training
While fine-tuned language models perform well on many tasks, they were a...
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10/12/2022
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm
Recently, the community has achieved substantial progress on many common...
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07/28/2022
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Large amounts of training data are one of the major reasons for the high...
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09/24/2021
Text-based NP Enrichment
Understanding the relations between entities denoted by NPs in text is a...
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04/17/2021
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
We explore Few-Shot Learning (FSL) for Relation Classification (RC). Foc...
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04/16/2021
Back to Square One: Bias Detection, Training and Commonsense Disentanglement in the Winograd Schema
The Winograd Schema (WS) has been proposed as a test for measuring commo...
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03/02/2021
Contrastive Explanations for Model Interpretability
Contrastive explanations clarify why an event occurred in contrast to an...
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01/26/2021
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
Multilingual pretrained language models have demonstrated remarkable zer...
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10/16/2020
It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT
Recent works have demonstrated that multilingual BERT (mBERT) learns ric...
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10/12/2020
The Extraordinary Failure of Complement Coercion Crowdsourcing
Crowdsourcing has eased and scaled up the collection of linguistic annot...
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10/11/2020
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant ...
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10/11/2020
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Contextualized word representations, such as ELMo and BERT, were shown t...
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06/01/2020
When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions
A growing body of work makes use of probing in order to investigate the ...
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04/16/2020
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
The ability to control for the kinds of information encoded in neural re...
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04/06/2020
Evaluating NLP Models via Contrast Sets
Standard test sets for supervised learning evaluate in-distribution gene...
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12/31/2019
oLMpics – On what Language Model Pre-training Captures
Recent success of pre-trained language models (LMs) has spurred widespre...
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06/04/2019
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attrib...
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05/26/2019
Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
We provide the first computational treatment of fused-heads construction...
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08/20/2018
Adversarial Removal of Demographic Attributes from Text Data
Recent advances in Representation Learning and Adversarial Training seem...
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07/10/2018