Papers by Mingtong Liu
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)
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| Challenge: | Sentence matching is a key issue in natural language inference and paraphrase identification. |
| Approach: | They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer. |
| Outcome: | The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets. |
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)
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| Challenge: | Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data. |
| Approach: | They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure. |
| Outcome: | The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data. |
A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge . |
| Approach: | They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data. |
| Outcome: | The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge. |
Recovering Gold from Black Sand: Multilingual Dense Passage Retrieval with Hard and False Negative Samples (2022.emnlp-main)
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| Challenge: | Existing approaches to recover and utilize negative samples have not been effective on all languages. |
| Approach: | They propose a multilingual dense passage retrieval framework to recover and utilize negative samples. |
| Outcome: | The proposed framework outperforms strong sparse, dense and hybrid baselines on a high-quality dataset covering eleven typologically diverse languages. |
MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)
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| Challenge: | Sentence-level translation, document-level and terminology constrained translations are important in machine translation. |
| Approach: | They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning . |
| Outcome: | The proposed model improves translation memory, document-level translation, and document-constrained translation tasks. |
Long Text Generation with Topic-aware Discrete Latent Variable Model (2022.emnlp-main)
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| Challenge: | Recent work focuses on the modeling of discourse relation, resulting in discrete codes learning shallow semantics. |
| Approach: | They propose a topic-aware latent code-guided text generation model that encourages discrete codes to model information about topics. |
| Outcome: | The proposed model generates more topic-relevant and coherent texts. |