| Challenge: | Currently, on-device keyboards have limited memory and response time for word prediction . a proposed on-device neural language model based word prediction method is available for mobile devices . |
| Approach: | They propose an on-device neural language model based word prediction method that optimizes run-time memory and provides a real-time prediction environment. |
| Outcome: | The proposed model outperforms existing methods for word prediction in keystroke savings and word prediction rate and has been commercialized. |
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| Challenge: | a proposed word prediction model is developed for a chat application serving more than 100 million users. |
| Approach: | They propose a fast word predictor that reduces memory size and inference time on mobile devices. |
| Outcome: | The proposed model reduces memory size and inference time on a mobile device compared with a standard neural network . it achieves robust performance by learning on large text corpora and is available on microsoft's chat app . |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
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Revisiting Simple Neural Probabilistic Language Models (2021.naacl-main)
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| Challenge: | Recent advances in language modeling have been driven not only by advances in neural architectures, but also through hardware and optimization improvements. |
| Approach: | They revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003) which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. |
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ProSeqo: Projection Sequence Networks for On-Device Text Classification (D19-1)
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| Challenge: | ProSeqo is a novel on-device sequence model for text classification . it uses dynamic recurrent projections without the need to store or look up pre-trained embeddings. |
| Approach: | They propose a novel on-device sequence model for text classification using recurrent projections that uses dynamic recursion projections without the need to store or look up any pre-trained embeddings. |
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Pre-trained language model representations for language generation (N19-1)
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| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
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Extreme Model Compression for On-device Natural Language Understanding (2020.coling-industry)
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| Challenge: | Xu and Sarikaya et al., 2014) perform word-embedding compression with NLU task learning . their approach achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. |
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On-device Structured and Context Partitioned Projection Networks (P19-1)
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| Challenge: | A challenge in on-device text classification is to build highly accurate models that fit in small memory footprint and have low latency. |
| Approach: | They propose an on-device neural network which learns compact projection vectors from raw text using structured and context-dependent partition projections. |
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RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)
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| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
| Approach: | They present the first domain-adapted and fully-trained large language model for text-based recommendation. |
| Outcome: | The proposed model outperforms baseline models on rating prediction and sequential recommendation tasks. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
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| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
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Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
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| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
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