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pckmt's Introduction

Hi there 👋

I am an NLP algorithm engineer graduated from Xiamen University with bachelor degree (2015-2019) and Tianjin University with master degree (2019-2022).

My research interests include:

  • 🔭 clustering analysis (fuzzy clustering theory and linguistic clustering)
  • 🌱 machine translation (text-only and multimodal machine translation)
  • 👯 multimodal learning (pretraining technology and reasoning)
  • 🌱 large language modeling (infra, multilingual pretrain and efficient universal sft)

I am passionate about specializing in algorithms and fit them into practical applications.

Experiences

  • 📫 2023-09 - now : working on Foundational LLM Team, Alibaba Inc., towards the universal intelligence of LLM, especially on dialogue and searching.
  • 📫 2022-04 - 2023-09: worked on ByteDance AI Lab in the fields of multimodal/multilingual machine translation and multilingual LLM.
  • 📫 2021-07 - 2021-11: conducted research on semi-parametric MT as a NLP Research intern on Alibaba Damo Academy (One conference paper published).
  • 📫 2020-11 - 2021-02: participated in early NLP Migration Project on HUAWEI Ascend, our work was reported as a markable practice [wiki].
  • 📫 2020-05 - 2020-11: conducted research on translation quality estimation in corporation with OPPO Research (One paper under review).
  • 📫 2020-04 - 2020-09: conducted research on vison & language multimodal machine translation (One conference paper published).
  • 🤔 2019-09 - 2020-05: joined in TJUNLP lab and conducted research on vision & language commensense reasoning, finally stopped for the lack of computational resources.
  • 👯 2018-03 - 2019-09: joined in Optimization Machine Learning Team and studied Fuzzy Clustering Theory (major) and Mainfold Learning (secondary) (One journal paper published and another two journal papers collaborated).
  • 👯 2016-11 - 2018-09: joined the Drone Team in charge of the compute vision algorithm, won the second place in International Aerial Robotics Competition.

Representative Publications [google scholar]

  • Efficient Cluster-Based k-Nearest-Neighbor Machine Translation. ACL. 2022.
  • AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation. ACL Findings. 2021.
  • Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding. AAAI. 2021.
  • A Novel Fuzzy c-Means Clustering Algorithm Using Adaptive Norm. International Journal of Fuzzy Sytems. 2019.

GitHub Stats

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pckmt's Issues

请问算法时间空间复杂度大概是怎样的?

根据提供的日志资料来看整个训练流程完成一遍耗时不会太长,我之前用一个toy data进行测试后也可以很快完成。
但是当换成大规模数据后耗时大大增加,请问耗时随数据规模如何变化?
可选的prune datastore步骤是否会有助于加速后续步骤?

请问PCKMT是否依赖于多个domain?

您好,经过一段时间对PCKMT的研究发现这个工作是将多个domain各自做compact,那么如果只有一个domain的话是否和前作adaptive knn-mt没有区别?
另外数据集中的subtitles domain在两个工作中均没有涉及而只做了其余四个domain,请问是出于数据量的考虑吗?

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