11 citations · 44 across the 18 of their papers we have counts for
19 papers
Convex Dataset Valuation for Post-Training
Siqi Zeng, Christopher Jung, Rui Li +7
Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post-training. In practice, however, developers face constraints on compute, l…
Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals
Moritz Vandenhirtz, Kaveh Hassani, Shervin Ghasemlou +5
Sequential recommender systems rank relevant items by modeling a user's interaction history and computing the inner product between the resulting user representation and stored ite…
Towards measuring fairness in speech recognition: Fair-Speech dataset
Irina-Elena Veliche, Zhuangqun Huang, Vineeth Ayyat Kochaniyan +3
The current public datasets for speech recognition (ASR) tend not to focus specifically on the fairness aspect, such as performance across different demographic groups. This paper…
Mitigating Unintended Memorization in Language Models via Alternating Teaching
Zhe Liu, Xuedong Zhang, Fuchun Peng
Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data. W…
Group Personalized Federated Learning
Zhe Liu, Yue Hui, Fuchun Peng
Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of highly heterogene…
Modeling Dependent Structure for Utterances in ASR Evaluation
Zhe Liu, Fuchun Peng
The bootstrap resampling method has been popular for performing significance analysis on word error rate (WER) in automatic speech recognition (ASR) evaluation. To deal with depend…
Neural-FST Class Language Model for End-to-End Speech Recognition
Antoine Bruguier, Duc Le, Rohit Prabhavalkar +7
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transduce…
Private Language Model Adaptation for Speech Recognition
Zhe Liu, Ke Li, Shreyan Bakshi +1
Speech model adaptation is crucial to handle the discrepancy between server-side proxy training data and actual data received on local devices of users. With the use of federated l…
Model-Based Approach for Measuring the Fairness in ASR
Zhe Liu, Irina-Elena Veliche, Fuchun Peng
The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population. In any fairness measurement studie…
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Xiaohui Zhang, Vimal Manohar, David Zhang +7
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria. However, they have vastly different legacies and are usually implem…
Federated Marginal Personalization for ASR Rescoring
Zhe Liu, Fuchun Peng
We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federat…
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
Xiaohui Zhang, Frank Zhang, Chunxi Liu +8
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on thre…
Improving N-gram Language Models with Pre-trained Deep Transformer
Yiren Wang, Hongzhao Huang, Zhe Liu +4
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in infe…
An Empirical Study of Efficient ASR Rescoring with Transformers
Hongzhao Huang, Fuchun Peng
Neural language models (LMs) have been proved to significantly outperform classical n-gram LMs for language modeling due to their superior abilities to model long-range dependencie…
Efficient Dynamic WFST Decoding for Personalized Language Models
Jun Liu, Jiedan Zhu, Vishal Kathuria +1
We propose a two-layer cache mechanism to speed up dynamic WFST decoding with personalized language models. The first layer is a public cache that stores most of the static part of…
Training ASR models by Generation of Contextual Information
Kritika Singh, Dmytro Okhonko, Jun Liu +8
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales,…
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
Tianxing He, Jun Liu, Kyunghyun Cho +4
In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encod…
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Shaojun Wang, Dale Schuurmans, Fuchun Peng +1
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is differ…
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching
Ben Wellner, Andrew McCallum, Fuchun Peng +1
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an appr…