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Found 24 papers, 12 papers with code
It’s not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT
Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.
Text-based NP Enrichment
Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding.
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
We explore Few-Shot Learning (FSL) for Relation Classification (RC).
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema
To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation.
Contrastive Explanations for Model Interpretability
Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions.
Measuring and Improving Consistency in Pretrained Language Models
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning.
It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT
Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.
The Extraordinary Failure of Complement Coercion Crowdsourcing
In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon.
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic tasks.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
In this work, we point out the inability to infer behavioral conclusions from probing results and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models.
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
oLMpics -- On what Language Model Pre-training Captures
A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data.
Adversarial Removal of Demographic Attributes Revisited
Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on.
At Your Fingertips: Automatic Piano Fingering Detection
Automatic Piano Fingering is a hard task which computers can learn using data.
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attributes of objects and events.
Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
We provide the first computational treatment of fused-heads constructions (FH), focusing on the numeric fused-heads (NFH).
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Where's My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution
We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs).
Adversarial Removal of Demographic Attributes from Text Data
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation.
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations
Latent factor models for recommender systems represent users and items as low dimensional vectors.
