7 citations · 12 across the 6 of their papers we have counts for
7 papers
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Hao Zhang +13
The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that…
Dialect-robust Evaluation of Generated Text
Jiao Sun, Thibault Sellam, Elizabeth Clark +6
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producin…
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
Parker Riley, Timothy Dozat, Jan A. Botha +5
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation. The dataset consists of professional tr…
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Timothy Dozat +7
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in fo…
Universal Dependency Parsing from Scratch
Peng Qi, Timothy Dozat, Yuhao Zhang +1
This paper describes Stanford's system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks require…
Simpler but More Accurate Semantic Dependency Parsing
Timothy Dozat, Christopher D. Manning
While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships t…
Deep Biaffine Attention for Neural Dependency Parsing
Timothy Dozat, Christopher D. Manning
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regulariz…