Papers by Jacob Devlin
Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
QueryForm: A Simple Zero-shot Form Entity Query Framework (2023.findings-acl)
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Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister
| Challenge: | Form-like document understanding is a key yet under-investigated problem . endlessly training specialized models on new document types is not scalable in many practical scenarios. |
| Approach: | They propose to use large-scale query-entity pairs generated from form-like webpages to pre-train QueryForm. |
| Outcome: | The proposed framework sets state-of-the-art average F1 score on XFUND and Payment benchmarks. |
Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)
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| Challenge: | Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text. |
| Approach: | They propose a method for generating synthetic question answering corpora by combining question generation and answer extraction models and filtering the results to ensure roundtrip consistency. |
| Outcome: | The proposed model achieves exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2 and NQ. |
Zero-Shot Entity Linking by Reading Entity Descriptions (P19-1)
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| Challenge: | Existing approaches to link entities to unseen entities require in-domain labeled data. |
| Approach: | They propose a zero-shot entity linking task where mentions must be linked to unseen entities without in-domain labeled data. |
| Outcome: | The proposed task can generalize to unseen entities without metadata or alias tables . the proposed system improves over baselines, including BERT, on a new dataset . |
Multi-Vector Attention Models for Deep Re-ranking (2021.emnlp-main)
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| Challenge: | Document retrieval systems often use two styles of neural network models . dual encoder models are used for retrieval and deep re-ranking, while cross-attention models are typically used for shallow reranking. |
| Approach: | They propose a dual encoder and cross-attention neural network architectures that combine query and document representations to optimize retrieval accuracy. |
| Outcome: | The proposed architecture trades off retrieval accuracy with joint computation and offline document storage cost. |
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (N19-1)
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| Challenge: | Existing language representation models pre-train deep bidirectional representations from unlabeled text without significant task-specific architecture modifications. |
| Approach: | They propose a language representation model that pre-trains bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. |
| Outcome: | The proposed model achieves state-of-the-art results on eleven natural language processing tasks, pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement) |