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Found 14 papers, 2 papers with code
Investigating the Effect of Language Models in Sequence Discriminative Training for Neural Transducers
In this work, we investigate the effect of language models (LMs) with different context lengths and label units (phoneme vs. word) used in sequence discriminative training for phoneme-based neural transducers.
On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers
Empirically, we show that ILM subtraction and sequence discriminative training achieve similar effects across a wide range of experiments on Librispeech, including both MMI and minimum Bayes risk (MBR) criteria, as well as neural transducers and LMs of both full and limited context.
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers
Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech
Language barriers present a great challenge in our increasingly connected and global world.
Revisiting Checkpoint Averaging for Neural Machine Translation
Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models.
Is Encoder-Decoder Redundant for Neural Machine Translation?
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks.
A Semantic Alignment System for Multilingual Query-Product Retrieval
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models
The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets.
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views.
Self-Normalized Importance Sampling for Neural Language Modeling
Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transformer-Based Direct Hidden Markov Model for Machine Translation
The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks.
Towards a Better Understanding of Label Smoothing in Neural Machine Translation
In order to combat overfitting and in pursuit of better generalization, label smoothing is widely applied in modern neural machine translation systems.
Predicting and Using Target Length in Neural Machine Translation
Attention-based encoder-decoder models have achieved great success in neural machine translation tasks.
