An Empirical Evaluation of Encoder Architectures for Fast Real-Time Long Conversational Understanding
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915157571010560 |
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| author | Senthilnathan, Annamalai Arumae, Kristjan Khalilia, Mohammed Xing, Zhengzheng Colak, Aaron R. |
| author_facet | Senthilnathan, Annamalai Arumae, Kristjan Khalilia, Mohammed Xing, Zhengzheng Colak, Aaron R. |
| contents | Analyzing long text data such as customer call transcripts is a cost-intensive and tedious task. Machine learning methods, namely Transformers, are leveraged to model agent-customer interactions. Unfortunately, Transformers adhere to fixed-length architectures and their self-attention mechanism scales quadratically with input length. Such limitations make it challenging to leverage traditional Transformers for long sequence tasks, such as conversational understanding, especially in real-time use cases. In this paper we explore and evaluate recently proposed efficient Transformer variants (e.g. Performer, Reformer) and a CNN-based architecture for real-time and near real-time long conversational understanding tasks. We show that CNN-based models are dynamic, ~2.6x faster to train, ~80% faster inference and ~72% more memory efficient compared to Transformers on average. Additionally, we evaluate the CNN model using the Long Range Arena benchmark to demonstrate competitiveness in general long document analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Empirical Evaluation of Encoder Architectures for Fast Real-Time Long Conversational Understanding Senthilnathan, Annamalai Arumae, Kristjan Khalilia, Mohammed Xing, Zhengzheng Colak, Aaron R. Computation and Language Analyzing long text data such as customer call transcripts is a cost-intensive and tedious task. Machine learning methods, namely Transformers, are leveraged to model agent-customer interactions. Unfortunately, Transformers adhere to fixed-length architectures and their self-attention mechanism scales quadratically with input length. Such limitations make it challenging to leverage traditional Transformers for long sequence tasks, such as conversational understanding, especially in real-time use cases. In this paper we explore and evaluate recently proposed efficient Transformer variants (e.g. Performer, Reformer) and a CNN-based architecture for real-time and near real-time long conversational understanding tasks. We show that CNN-based models are dynamic, ~2.6x faster to train, ~80% faster inference and ~72% more memory efficient compared to Transformers on average. Additionally, we evaluate the CNN model using the Long Range Arena benchmark to demonstrate competitiveness in general long document analysis. |
| title | An Empirical Evaluation of Encoder Architectures for Fast Real-Time Long Conversational Understanding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.12458 |