A Challenge Set for Advancing Language Modeling

By Geoffry Zweig, C. Burges. 2012

38 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Bansal
2. Yoav Goldberg
3. Douwe Kiela
4. Wen-tau Yih
5. Yonatan Belinkov
6. Jianfeng Gao
7. Jason Aaron Edward Weston
8. Sebastian Riedel

In Evaluation & Benchmarks

1. Wen-tau Yih
2. Divyansh Kaushik
3. Zachary Chase Lipton
4. Douwe Kiela
5. Andriy Mnih
6. Koray Kavukcuoglu
7. Arianna Bisazza
8. Christof Monz

Citations in the corpus

• MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, background): “More recently, Zweig and Burges (2012) provided a set of 1040 sentences that comprise an SAT-style multiple choice sentence completion task.”
• Learning word embeddings efficiently with noise-contrastive estimation (2013, background): “Microsoft Research (MSR) has released two challenge sets: a set of sentences each with a missing word to be filled in [20] and a set of analogy questions [11], designed to evaluate semantic and syntactic content of word representations respectively.”
• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study (2021, methodology): “In an early example of model-in-the-loop data collection, Zweig and Burges (2012) use n -gram language models to suggest candidate incorrect answers for a fill-in-the-blank task.”
• Recurrent Memory Networks for Language Modeling (2016, methodology): “, 2013)4 which is far below human accuracy of 91% (Zweig and Burges, 2012).”