Yoav Artzi

Cornell University
https://yoavartzi.com
Publications53
h-index29
Citations3,983
Highly Influential Citations653
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BERTScore: Evaluating Text Generation with BERT
TLDR
This work proposes BERTScore, an automatic evaluation metric for text generation that correlates better with human judgments and provides stronger model selection performance than existing metrics. Expand
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
TLDR
The NEWSROOM dataset is presented, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications between 1998 and 2017, and the summaries combine abstractive and extractive strategies. Expand
A Corpus for Reasoning about Natural Language Grounded in Photographs
TLDR
This work introduces a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges, and Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge. Expand
Scaling Semantic Parsers with On-the-Fly Ontology Matching
TLDR
A new semantic parsing approach that learns to resolve ontological mismatches, which is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Expand
Learning to Automatically Solve Algebra Word Problems
TLDR
An approach for automatically learning to solve algebra word problems by reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers to the problem text. Expand
Training RNNs as Fast as CNNs
TLDR
The Simple Recurrent Unit architecture is proposed, a recurrent unit that simplifies the computation and exposes more parallelism, and is as fast as a convolutional layer and 5-10x faster than an optimized LSTM implementation. Expand
TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
TLDR
This work introduces the Touchdown task and dataset, where an agent must first follow navigation instructions in a Street View environment to a goal position, and then guess a location in its observed environment described in natural language to find a hidden object. Expand
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
TLDR
This paper shows semantic parsing can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. Expand
A Corpus of Natural Language for Visual Reasoning
TLDR
A method of crowdsourcing linguistically-diverse data, and an analysis of the data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. Expand
Revisiting Few-sample BERT Fine-tuning
TLDR
It is found that parts of the BERT network provide a detrimental starting point for fine-tuning, and simply re-initializing these layers speeds up learning and improves performance. Expand
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