Papers by Daniel Pressel
Lightweight Transformers for Conversational AI (2022.naacl-industry)
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| Challenge: | Commercial dialogue systems typically require a small footprint and fast execution time, but recent trends are in the other direction, resulting in difficulties in model deployment. |
| Approach: | They build Transformer-based Language Models from scratch on large corpora of conversational data and compare their performance against BERT and other strong baselines on dialogue probing tasks. |
| Outcome: | The proposed model outperforms existing models on dialogue probing tasks and can be fine-tuned on a single consumer GPU card. |
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
| Outcome: | The proposed model trains twice as fast as a CRF with statistically insignificant differences in F1 . the proposed model is open source and can be used in PyTorch and TensorFlow. |
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining (2022.naacl-industry)
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| Challenge: | Existing approaches to clustering unlabeled utterances are based on transformerbased sentence embedding methods. |
| Approach: | They propose a semantic embedding approach that can be leveraged to identify clusters of utterances that correspond to unhandled intents. |
| Outcome: | The proposed approach can identify clusters of utterances that correspond to unhandled intents from a large collection of enterprise virtual assistant data using a multi-task softmax loss. |
An Effective Label Noise Model for DNN Text Classification (N19-1)
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| Challenge: | Existing methods to train deep neural networks with label noise are limited to image classification models . label noise is important because of the large number of errors and errors in training datasets . |
| Approach: | They propose a non-linear processing layer that models label noise into a convolutional neural network (CNN) they add a noise model layer on top of their target model to account for label noise . |
| Outcome: | The proposed approach is robust to label noise and can learn better sentences . it is based on extensive experiments on text classification datasets . |