Papers by Jingfang Xu
A Compact and Language-Sensitive Multilingual Translation Method (P19-1)
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| Challenge: | Existing paradigms for multilingual neural machine translation do not make full use of language commonality and parameter sharing. |
| Approach: | They propose a multilingual neural machine translation paradigm with one encoder-decoder model that makes full use of language commonality and parameter sharing. |
| Outcome: | The proposed method outperforms strong standard multilingual translation systems on WMT and IWSLT datasets. |
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction (2020.acl-main)
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| Challenge: | Existing models for machine reading comprehension lack evidence labels for training models. |
| Approach: | They propose a method which supervises the evidence extractor with auto-generated evidence labels in an iterative process. |
| Outcome: | The proposed method improves on three MRC tasks on seven datasets. |
Learning to Copy for Automatic Post-Editing (D19-1)
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| Challenge: | Automatic post-editing (APE) is an important task in natural language processing. |
| Approach: | They propose a method that explicitly models how to copy words from a machine translation to a correct translation. |
| Outcome: | The proposed method outperforms all published methods on the WMT 2016-2017 datasets. |
Transfer Learning for Sequence Generation: from Single-source to Multi-source (2021.acl-long)
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| Challenge: | Recent studies have shown that pretrained models are effective for low-resource downstream tasks. |
| Approach: | They propose a two-stage finetuning method to transfer pretrained models to MSG tasks by concatenating multiple sources into a single long sequence. |
| Outcome: | The proposed model outperforms baselines on the WMT17 APE task and multi-source translation task using the WTM14 test set. |
Multi-Lingual Question Generation with Language Agnostic Language Model (2021.findings-acl)
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| Challenge: | Existing training data for question generation in English and Chinese is limited . a language-agnostic model is developed to learn the shared representation from several languages in a single architecture. |
| Approach: | They propose a language-agnostic language model which learns the shared representation from several languages in a single architecture. |
| Outcome: | The proposed model improves multi-lingual question generation over five languages. |
Improving the Transformer Translation Model with Document-Level Context (D18-1)
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| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
| Outcome: | Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly. |
Three Strategies to Improve One-to-Many Multilingual Translation (D18-1)
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| Challenge: | Existing studies show that one-to-many multilingual translation cannot perform on par with the individually trained models. |
| Approach: | They propose to exploit unique initial states for target languages and language-dependent positional embeddings to create hidden cells of the encoder to achieve comparable or even better performance than individually trained models. |
| Outcome: | The proposed methods achieve comparable or even better performance than the individually trained models. |