activity
20162020
most citedEvaluating Models' Local Decision Boundaries via Contrast Sets

44 citations · 60 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2020

DRTS Parsing with Structure-Aware Encoding and Decoding

Qiankun Fu, Yue Zhang, Jiangming Liu +1

Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a ne…

cs.CL2020★ 12 cited

Multi-Step Inference for Reasoning Over Paragraphs

Jiangming Liu, Matt Gardner, Shay B. Cohen +1

Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives. Prior work has largely tried to do this either symbolically o…

cs.CL2020★ 44 cited

Evaluating Models' Local Decision Boundaries via Contrast Sets

Matt Gardner, Yoav Artzi, Victoria Basmova +23

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…

cs.CL2017★ 1 cited

In-Order Transition-based Constituent Parsing

Jiangming Liu, Yue Zhang

Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize p…

cs.CL2017★ 1 cited

Encoder-Decoder Shift-Reduce Syntactic Parsing

Jiangming Liu, Yue Zhang

Starting from NMT, encoder-decoder neu- ral networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the en- coder components achieve…

cs.CL2016★ 2 cited

Shift-Reduce Constituent Parsing with Neural Lookahead Features

Jiangming Liu, Yue Zhang

Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a p…