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Bootstrapping Relation Extractors using Syntactic Search by Examples
The advent of neural-networks in NLP brought with it substantial improve...
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A simple geometric proof for the benefit of depth in ReLU networks
We present a simple proof for the benefit of depth in multi-layer feedfo...
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Facts2Story: Controlling Text Generation by Key Facts
Recent advancements in self-attention neural network architectures have ...
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Parameter Norm Growth During Training of Transformers
The capacity of neural networks like the widely adopted transformer is k...
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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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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Trust is a central component of the interaction between people and AI, i...
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The Extraordinary Failure of Complement Coercion Crowdsourcing
Crowdsourcing has eased and scaled up the collection of linguistic annot...
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Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Contextualized word representations, such as ELMo and BERT, were shown t...
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Relation Extraction as Two-way Span-Prediction
The current supervised relation classification (RC) task uses a single e...
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Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
The process of collecting and annotating training data may introduce dis...
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Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
To what extent can a neural network systematically reason over symbolic ...
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Interactive Extractive Search over Biomedical Corpora
We present a system that allows life-science researchers to search a lin...
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Syntactic Search by Example
We present a system that allows a user to search a large linguistically ...
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Aligning Faithful Interpretations with their Social Attribution
We find that the requirement of model interpretations to be faithful is ...
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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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Nakdan: Professional Hebrew Diacritizer
We present a system for automatic diacritization of Hebrew text. The sys...
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pyBART: Evidence-based Syntactic Transformations for IE
Syntactic dependencies can be predicted with high accuracy, and are usef...
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A Two-Stage Masked LM Method for Term Set Expansion
We tackle the task of Term Set Expansion (TSE): given a small seed set o...
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A Formal Hierarchy of RNN Architectures
We develop a formal hierarchy of the expressive capacity of RNN architec...
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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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Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
With the growing popularity of deep-learning based NLP models, comes a n...
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Unsupervised Domain Clusters in Pretrained Language Models
The notion of "in-domain data" in NLP is often over-simplistic and vague...
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Break It Down: A Question Understanding Benchmark
Understanding natural language questions entails the ability to break do...
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oLMpics – On what Language Model Pre-training Captures
Recent success of pre-trained language models (LMs) has spurred widespre...
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How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?
Many natural languages assign grammatical gender also to inanimate nouns...
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Learning Deterministic Weighted Automata with Queries and Counterexamples
We present an algorithm for extraction of a probabilistic deterministic ...
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Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
We consider the situation in which a user has collected a small set of d...
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Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
We follow the step-by-step approach to neural data-to-text generation we...
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Transfer Learning Between Related Tasks Using Expected Label Proportions
Deep learning systems thrive on abundance of labeled training data but s...
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Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Crowdsourcing has been the prevalent paradigm for creating natural langu...
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Ab Antiquo: Proto-language Reconstruction with RNNs
Historical linguists have identified regularities in the process of hist...
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Towards better substitution-based word sense induction
Word sense induction (WSI) is the task of unsupervised clustering of wor...
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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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Towards Neural Decompilation
We address the problem of automatic decompilation, converting a program ...
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Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Data-to-text generation can be conceptually divided into two parts: orde...
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Aligning Vector-spaces with Noisy Supervised Lexicons
The problem of learning to translate between two vector spaces given a s...
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Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages
How do typological properties such as word order and morphological case ...
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Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Word embeddings are widely used in NLP for a vast range of tasks. It was...
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Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark info...
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A Little Is Enough: Circumventing Defenses For Distributed Learning
Distributed learning is central for large-scale training of deep-learnin...
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Assessing BERT's Syntactic Abilities
I assess the extent to which the recently introduced BERT model captures...
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Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training
We focus on the problem of language modeling for code-switched language,...
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Understanding Convolutional Neural Networks for Text Classification
We present an analysis into the inner workings of Convolutional Neural N...
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Can LSTM Learn to Capture Agreement? The Case of Basque
Sequential neural networks models are powerful tools in a variety of Nat...
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Word Sense Induction with Neural biLM and Symmetric Patterns
An established method for Word Sense Induction (WSI) uses a language mod...
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Adversarial Removal of Demographic Attributes from Text Data
Recent advances in Representation Learning and Adversarial Training seem...
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Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
We present SetExpander, a corpus-based system for expanding a seed set o...
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Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Deep Reinforcement Learning has managed to achieve state-of-the-art resu...
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On the Practical Computational Power of Finite Precision RNNs for Language Recognition
While Recurrent Neural Networks (RNNs) are famously known to be Turing c...
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Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
We create a new NLI test set that shows the deficiency of state-of-the-a...
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