Papers by Timothy Dozat
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation (2023.tacl-1)
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Parker Riley, Timothy Dozat, Jan A. Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, Noah Constant
| Challenge: | a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation is presented . FRMT is a type of style-targeted translation that uses labeled training data to perform tasks. |
| Approach: | They propose a dataset and evaluation benchmark for Few-shot Region-aware Machine Translation. |
| Outcome: | The proposed model is based on two translations from English into Portuguese and Mandarin Chinese. |
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction (2022.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister
| Challenge: | Form-like document understanding is a surging research topic due to its practical applications . form documents have unique challenges stemming from their structural characteristics . |
| Approach: | They propose a structure-aware sequence model that leverages spatial relationships between tokens in a form for more precise attention score calculation. |
| Outcome: | The proposed model outperforms existing methods with a more compact model size and less pre-training data. |
Simpler but More Accurate Semantic Dependency Parsing (P18-2)
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| Challenge: | Syntactic dependency parsing is the most popular method for automatically extracting low-level relationships between words in a sentence. |
| Approach: | They extend a syntactic dependency parser to train on and generate graph-structured representations that capture between-word relationships that are more closely related to the meaning of a sentence. |
| Outcome: | The proposed system beats the current state-of-the-art system by 0.6% and linguistically richer representations push the margin even higher. |
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
| Challenge: | Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. |
| Approach: | They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. |
| Outcome: | The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. |
Dialect-robust Evaluation of Generated Text (2023.acl-long)
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Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
| Challenge: | Existing evaluation metrics that are not robust to dialect variation are difficult to measure for many groups of users and can penalize systems for producing text in lower-resource dialects. |
| Approach: | They propose a dialect-robust evaluation metric that produces the same score for system outputs that share the same semantics but are expressed in different dialects. |
| Outcome: | The proposed method significantly improves dialect robustness while preserving the correlation between automated metrics and human ratings. |