9 accepted papers
Large language models (LLMs) have shown excellent performance on general machine translation. However, LLMs suffer from high deployment cost and unsatisfying quality on low-resource domains. To this end, we explore to build base translation models with LLM-enhanced data augmentation. For data augmen…
Dealing with language heterogeneity has always been one of the challenges in neural machine translation (NMT).The idea of using mixture-of-experts (MoE) naturally excels in addressing this issue by employing different experts to take responsibility for different problems.However, the parameter-ineff…
This paper describes SRCB-LUL team’s person identification system submitted to track 1 of the ICASSP 2023 Person Identification and Relapse Detection from Continuous Recordings of Biosignals (e-Prevention) challenge, which aims to identify the wearer of the smartwatch. At first, abnormal values of t…
Text image machine translation (TIMT) which translates source language text images into target language texts has attracted intensive attention in recent years. Although the end-to-end TIMT model directly generates target translation from encoded text image features with an efficient architecture, i…
Knowledge transfer can boost neural machine translation (NMT), for example, by finetuning a pretrained masked language model (LM). However, it may suffer from the forgetting problem and the structural inconsistency between pretrained LMs and NMT models. Knowledge distillation (KD) may be a potential…
To improve the performance of Automatic Speech Recognition (ASR), it is common to deploy an error correction module at the post-processing stage to correct recognition errors. In this paper, we propose 1) an error correction model, which takes account of both contextual information and phonetic info…
Self-attention mechanism has been shown to be an effective approach for capturing global context dependencies in sequence modeling, but it suffers from quadratic complexity in time and memory usage. Due to the sparsity of the attention matrix, much computation is redundant. Therefore, in this paper,…
Large model size and high computational complexity prevent the neural machine translation (NMT) models from being deployed to low resource devices (e.g. mobile phones). Due to the large vocabulary, a large storage memory is required for the word embedding matrix in NMT models, in the meantime, high…
In machine translation, vocabulary manipulation is a way to reduce the target vocabulary based on the source sentence and the word dictionary, which is effective to lower latency during inference for text translation in industrial application. But vocabulary manipulation is hard to apply to the end-…