no code implementations • 26 Mar 2023 • Thuy-Trang Vu, Xuanli He, Gholamreza Haffari, Ehsan Shareghi
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
no code implementations • 20 Oct 2022 • Thuy-Trang Vu, Shahram Khadivi, Xuanli He, Dinh Phung, Gholamreza Haffari
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
1 code implementation • 15 Sep 2022 • Terry Yue Zhuo, Qiongkai Xu, Xuanli He, Trevor Cohn
Round-trip translation could be served as a clever and straightforward technique to alleviate the requirement of the parallel evaluation corpus.
1 code implementation • 5 Dec 2021 • Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, Chenguang Wang
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day.
no code implementations • 30 Oct 2021 • Xuanli He, Iman Keivanloo, Yi Xu, Xiang He, Belinda Zeng, Santosh Rajagopalan, Trishul Chilimbi
To achieve this, we propose a novel idea, Magic Pyramid (MP), to reduce both width-wise and depth-wise computation via token pruning and early exiting for Transformer-based models, particularly BERT.
no code implementations • 29 Sep 2021 • Xuanli He, Islam Nassar, Jamie Ryan Kiros, Gholamreza Haffari, Mohammad Norouzi
To obtain strong task-specific generative models, we either fine-tune a large language model (LLM) on inputs from specific tasks, or prompt a LLM with a few input examples to generate more unlabeled examples.
1 code implementation • EMNLP 2021 • Thuy-Trang Vu, Xuanli He, Dinh Phung, Gholamreza Haffari
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
no code implementations • COLING 2022 • Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, Gholamreza Haffari
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models.
1 code implementation • 11 Jun 2021 • Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, Mohammad Norouzi
This paper studies the use of language models as a source of synthetic unlabeled text for NLP.
no code implementations • 23 May 2021 • Chen Chen, Xuanli He, Lingjuan Lyu, Fangzhao Wu
In this work, we bridge this gap by first presenting an effective model extraction attack, where the adversary can practically steal a BERT-based API (the target/victim model) by only querying a limited number of queries.
1 code implementation • NAACL 2021 • Xuanli He, Lingjuan Lyu, Qiongkai Xu, Lichao Sun
Finally, we investigate two defence strategies to protect the victim model and find that unless the performance of the victim model is sacrificed, both model ex-traction and adversarial transferability can effectively compromise the target models
no code implementations • 1 Jan 2021 • Xuanli He, Lingjuan Lyu, Lichao Sun, Xiaojun Chang, Jun Zhao
We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Trung Bui, Zhe Lin, Franck Dernoncourt, Nhan Dam
In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user's command.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Lingjuan Lyu, Xuanli He, Yitong Li
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy.
no code implementations • 25 Jun 2020 • Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model.
1 code implementation • ACL 2020 • Xuanli He, Gholamreza Haffari, Mohammad Norouzi
This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units.
no code implementations • CONLL 2018 • Xuanli He, Gholamreza Haffari, Mohammad Norouzi
In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model.
no code implementations • ALTA 2018 • Xuanli He, Quan Hung Tran, William Havard, Laurent Besacier, Ingrid Zukerman, Gholamreza Haffari
In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i. e. human transcriptions, instead of Automatic Speech Recognition (ASR)'s transcriptions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • WS 2017 • Ekaterina Vylomova, Trevor Cohn, Xuanli He, Gholamreza Haffari
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation.