66 citations · 418 across the 62 of their papers we have counts for
21 papers · 1 filter
Facts2Story: Controlling Text Generation by Key Facts
Eyal Orbach, Yoav Goldberg
Recent advancements in self-attention neural network architectures have raised the bar for open-ended text generation. Yet, while current methods are capable of producing a coheren…
It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT
Hila Gonen, Shauli Ravfogel, Yanai Elazar +1
Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translati…
The Extraordinary Failure of Complement Coercion Crowdsourcing
Yanai Elazar, Victoria Basmov, Shauli Ravfogel +2
Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the comple…
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
Shachar Rosenman, Alon Jacovi, Yoav Goldberg
The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We ident…
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent
William Merrill, Vivek Ramanujan, Yoav Goldberg +2
The capacity of neural networks like the widely adopted transformer is known to be very high. Evidence is emerging that they learn successfully due to inductive bias in the trainin…
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller +1
Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely,…
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations
Shauli Ravfogel, Yanai Elazar, Jacob Goldberger +1
Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic tasks. In this work, we tackle the task of unsupervised dis…
Relation Classification as Two-way Span-Prediction
Amir DN Cohen, Shachar Rosenman, Yoav Goldberg
The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat t…
Interactive Extractive Search over Biomedical Corpora
Hillel Taub-Tabib, Micah Shlain, Shoval Sadde +4
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using pat…
Syntactic Search by Example
Micah Shlain, Hillel Taub-Tabib, Shoval Sadde +1
We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this e…
pyBART: Evidence-based Syntactic Transformations for IE
Aryeh Tiktinsky, Yoav Goldberg, Reut Tsarfaty
Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be im…
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark +2
To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but t…
Aligning Faithful Interpretations with their Social Attribution
Alon Jacovi, Yoav Goldberg
We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case-study, we present several failure…
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi +1
A growing body of work makes use of probing to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitatio…
Nakdan: Professional Hebrew Diacritizer
Avi Shmidman, Shaltiel Shmidman, Moshe Koppel +1
We present a system for automatic diacritization of Hebrew text. The system combines modern neural models with carefully curated declarative linguistic knowledge and comprehensive…
A Two-Stage Masked LM Method for Term Set Expansion
Guy Kushilevitz, Shaul Markovitch, Yoav Goldberg
We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical ut…
Unsupervised Domain Clusters in Pretrained Language Models
Roee Aharoni, Yoav Goldberg
The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In a…
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen +2
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.…
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
Alon Jacovi, Yoav Goldberg
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretat…
A Formal Hierarchy of RNN Architectures
William Merrill, Gail Weiss, Yoav Goldberg +3
We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN's memory, a…
Break It Down: A Question Understanding Benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta +4
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decom…