Papers by Jiangtao Feng
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)
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| Challenge: | Existing pre-training methods are not effective for machine translation tasks. |
| Approach: | They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space. |
| Outcome: | The proposed approach improves translation quality on low, medium, rich resource languages. |
R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)
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YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou
| Challenge: | Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning. |
| Approach: | They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task. |
| Outcome: | The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent. |
| Approach: | They propose a probabilistic model for document-level relation extraction by learning logic rules. |
| Outcome: | The proposed model outperforms baseline models in relation performance and logical consistency. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to text generation use discrete text within a continuous diffusion space, which incurs substantial computational overhead during training and results in slower sampling speeds. |
| Approach: | They propose a soft absorbing state that facilitates diffusion models in learning to reconstruct discrete mutations based on the underlying Gaussian space. |
| Outcome: | The proposed method accelerates training convergence by 4x and generates samples of similar quality 800x faster, rendering it closer to practical application. |
Counter-Interference Adapter for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing approaches to multilingual machine translation suffer from performance degradation, resulting in a single model being inferior to separately trained bilingual models on resource-rich languages. |
| Approach: | They propose a transformer-based model with a small parameter overhead for multilingual machine translation that outperforms strong multilingual baselines on 64 of 66 language directions. |
| Outcome: | The proposed model outperforms strong multilingual baselines on 64 of 66 language directions, 42 of which have above 0.5 BLEU improvement. |
OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)
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| Challenge: | In-context Learning (ICL) is a new paradigm for large language model evaluation. |
| Approach: | They propose an open-source toolkit for ICL and LLM evaluation. |
| Outcome: | The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs. |
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (2022.findings-emnlp)
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| Challenge: | Recent work on dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs). |
| Approach: | They propose a progressive zero-shot dataset generation framework which leverages feedback from the task-specific model to guide the generation of new training data via in-context examples. |
| Outcome: | The proposed framework achieves on-par or superior performance with only 1% synthetic dataset size, when compared to baseline methods without in-context feedback. |