most citedLanguage Model Pre-training for Hierarchical Document Representations

18 citations · 44 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL201918 cited

Language Model Pre-training for Hierarchical Document Representations

Ming-Wei Chang, Kristina Toutanova, Kenton Lee +1

Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentati…

cs.CL20168 cited

Answering Complicated Question Intents Expressed in Decomposed Question Sequences

Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between t…

cs.CL20164 cited

Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities

Yi Yang, Ming-Wei Chang, Jacob Eisenstein

Entity linking is the task of identifying mentions of entities in text, and linking them to entries in a knowledge base. This task is especially difficult in microblogs, as there i…

cs.CL20167 cited

S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

Yi Yang, Ming-Wei Chang

Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom stud…

cs.CL20167 cited

Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems

Shyam Upadhyay, Ming-Wei Chang

We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solver…