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Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.
“Improved backing-off for m-gram language modeling,”
Reinhard Kneser and Hermann Ney, · 1995
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“Improved neural network based language modelling and adaptation,”
Junho Park, Xunying Liu, Mark JF Gales, and Phil C Woodland, · 2010
Earlier work this paper cites.
“Sequence transduction with recurrent neural networks,”
Alex Graves, · 2012
Earlier work this paper cites.
“Multi-domain neural network language model.,”
Tanel Alumäe, · 2013
Earlier work this paper cites.
“Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,”
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals, · 2016
Earlier work this paper cites.
“Pointer sentinel mixture models,”
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher, · 2016
Earlier work this paper cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2018
Earlier work this paper cites.
“Language models are unsupervised multitask learners,”
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al., · 2019
Cited alongside, same era.
“Language modeling with deep transformers,”
Kazuki Irie, Albert Zeyer, Ralf Schlüter, and Hermann Ney, · 2019
Cited alongside, same era.
“Dialogpt: Large-scale generative pre-training for conversational response generation,”
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan, · 2019
Cited alongside, same era.
“Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss,”
Qian Zhang, Han Lu, Hasim Sak, Anshuman Tripathi, Erik McDermott, Stephen Koo, and Shankar Kumar, · 2020
Cited alongside, same era.
“Adapting Long Context NLM for ASR Rescoring in Conversational Agents,”
Ashish Shenoy, Sravan Bodapati, Monica Sunkara, Srikanth Ronanki, and Katrin Kirchhoff, · 2021
Closest in time.
“Asr adaptation for e-commerce chatbots using cross-utterance context and multi-task language modeling,”
Ashish Shenoy, Sravan Bodapati, and Katrin Kirchhoff, · 2021
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“Remember the context! asr slot error correction through memorization,”
Dhanush Bekal, Ashish Shenoy, Monica Sunkara, Sravan Bodapati, and Katrin Kirchhoff, · 2021
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“The power of scale for parameter-efficient prompt tuning,”
Brian Lester, Rami Al-Rfou, and Noah Constant, · 2021
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“Prefix-tuning: Optimizing continuous prompts for generation,”
Xiang Lisa Li and Percy Liang, · 2021
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Mahaveer Jain, Gil Keren, Jay Mahadeokar, Geoffrey Zweig, Florian Metze, and Yatharth Saraf, · 2020
Cited alongside, same era.
“Adapterhub: A framework for adapting transformers,”
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych, · 2020
Cited alongside, same era.
“Language models are few-shot learners,”
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al., · 2020
Cited alongside, same era.
“Efficient domain adaptation of language models in ASR systems using prompt-tuning,”
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, and Katrin Kirchhoff, · 2021
Closest in time.
“Listen, know and spell: Knowledge-infused subword modeling for improving asr performance of oov named entities,”
Nilaksh Das, Duen Horng Chau, Monica Sunkara, Sravan Bodapati, Dhanush Bekal, and Katrin Kirchhoff, · 2022
Closest in time.