Papers by David Nahamoo
Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems (N19-2)
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| Challenge: | Existing toolsets are incomplete in meeting the goal of building effective dialog systems, authors say . |
| Approach: | They compare dialog tools available from a number of companies to determine their strengths and weaknesses . they provide quantitative and qualitative results in three main areas: natural language understanding, dialog, and text generation . |
| Outcome: | The toolsets are incomplete, but they are compared to other tools to determine their strengths and weaknesses. |
Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
| Approach: | They propose a method to generate synthetic QA pairs for unsupervised self adaptation . they use massive amounts of data to simulate self-supervised tasks . |
| Outcome: | The proposed method improves QA systems significantly by using less data and training computation than existing augmentation approaches. |
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Named entity recognition tasks require a self-attention mechanism with unconstrained length that fails to capture local dependencies. |
| Approach: | They propose a joint training objective which better captures the semantics of words corresponding to the same entity by augmenting the objective with a group-consistency loss component. |
| Outcome: | The proposed model achieves a test F1 of 93.98 with a single transformer model. |