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Found 20 papers, 8 papers with code
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
Rethinking Round-Trip Translation for Machine Translation Evaluation
Round-trip translation could be served as a clever and straightforward technique to alleviate the requirement of the parallel evaluation corpus.
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
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.
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning
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.
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
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.
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
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.
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models.
Generate, Annotate, and Learn: NLP with Synthetic Text
This paper studies the use of language models as a source of synthetic unlabeled text for NLP.
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs
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.
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
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
EXPLORING VULNERABILITIES OF BERT-BASED APIS
We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.
Scene Graph Modification Based on Natural Language Commands
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.
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
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.
Towards Differentially Private Text Representations
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.
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation
This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units.
Sequence to Sequence Mixture Model for Diverse Machine Translation
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.
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
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
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation.
