ConveRT: Efficient and Accurate Conversational Representations from Transformers (1911.03688v2)

Abstract: General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. We show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.

• The paper introduces a lightweight ConveRT model using a dual-encoder architecture and 8-bit embedding quantization to reduce computation and model size.
• It achieves state-of-the-art response selection on benchmarks like Reddit, AmazonQA, and DSTC7 Ubuntu by leveraging multi-context encoding.
• The model shows strong transfer learning capabilities in tasks such as intent classification, broadening its applicability in various NLP domains.

Overview of ConveRT: Efficient and Accurate Conversational Representations from Transformers

This paper introduces ConveRT, a novel pretraining framework optimized for real-world conversational AI applications. Traditional sentence encoders like BERT, though effective for general NLP tasks, are often computationally expensive and slow, making them less ideal for dialogue-based applications. The ConveRT model addresses these challenges by offering a lightweight and efficient alternative specifically geared towards conversational tasks.

Methodology and Innovation

ConveRT leverages a dual-encoder architecture for response selection tasks, which inherently makes it more suitable for dialogs. This architecture efficiently uses the Transformer model setup with innovations such as 8-bit embedding quantization and subword-level parameterization, substantially reducing the model's footprint to 59MB. The design choices render ConveRT more memory- and energy-efficient compared to traditional models.

The proposed model is pretrained using a retrieval-based response selection task, utilizing extensive natural conversational datasets like Reddit. This approach ensures that ConveRT effectively captures conversational cues, enhancing its capacity to select appropriate responses based on dialog history. Notably, the paper introduces multi-context encoding, which expands the model's ability to comprehend full dialog histories rather than only the immediate preceding exchanges, offering significant performance gains.

Empirical Results

ConveRT achieves state-of-the-art performance in various established response selection benchmarks. In single-context setups, the model outperforms competitive dual-encoder architectures, demonstrating its efficacy across datasets such as Reddit, AmazonQA, and DSTC7 Ubuntu. The multi-context variant further enhances performance by leveraging entire dialog histories instead of relying solely on immediate context, showcasing the robustness of the proposed framework.

Transfer Learning and Applicability

Beyond response selection, ConveRT also demonstrates strong potential in transfer applications. The encoded representations can be directly transferred to tasks like intent classification, showing competitive performance against prominent models like BERT and USE. This adaptability suggests that the framework can be extended beyond dialog systems into other NLP domains, especially where data is scarce.

Implications and Future Prospects

The implications of ConveRT's design are promising for the broader conversational AI domain. Its reduced computational cost and rapid training times (18 hours at approximately \$85) underscore a crucial point: accessibility to effective AI models does not necessarily demand extensive resources. This democratizes access for researchers and practitioners working in environments with limited computational capabilities.

Moreover, the paper sets a precedent in efficiently aligning model architecture with task-specific requirements, pushing the boundaries of how lightweight models can still provide top-tier performance. As AI continues to permeate more facets of practical applications, the scalable and portable nature of ConveRT positions it as a particularly valuable tool.

Conclusion

ConveRT represents a significant advancement in making conversational AI more accessible and efficient without sacrificing performance. Its compact size, efficient training methodology, and high portability make it an exemplary model in retrieval-based dialogue processing. Future work could explore extending its applications to a broader array of NLP tasks, as well as refining the multi-context approach to augment interpretability and effectiveness further. The open release of ConveRT models encourages continued exploration and optimization within the NLP community.