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Graph Integrated Language Transformers for Next Action Prediction in Complex Phone Calls (2404.08155v1)

Published 11 Apr 2024 in cs.CL

Abstract: Current Conversational AI systems employ different machine learning pipelines, as well as external knowledge sources and business logic to predict the next action. Maintaining various components in dialogue managers' pipeline adds complexity in expansion and updates, increases processing time, and causes additive noise through the pipeline that can lead to incorrect next action prediction. This paper investigates graph integration into language transformers to improve understanding the relationships between humans' utterances, previous, and next actions without the dependency on external sources or components. Experimental analyses on real calls indicate that the proposed Graph Integrated Language Transformer models can achieve higher performance compared to other production level conversational AI systems in driving interactive calls with human users in real-world settings.

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References (36)
  1. Duygu Altinok. 2018. An ontology-based dialogue management system for banking and finance dialogue systems. arXiv preprint arXiv:1804.04838.
  2. Rasa: Open source language understanding and dialogue management.
  3. Action-based conversations dataset: A corpus for building more in-depth task-oriented dialogue systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3002–3017.
  4. Bert for joint intent classification and slot filling. arXiv preprint arXiv:1902.10909.
  5. Traum David. 2017. Computational approaches to dialogue. In The Routledge Handbook of Language and Dialogue, pages 143–161. Routledge.
  6. Talk the walk: Navigating grids in new york city through grounded dialogue.
  7. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  8. Neural path hunter: Reducing hallucination in dialogue systems via path grounding. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2197–2214.
  9. Slot-gated modeling for joint slot filling and intent prediction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 753–757.
  10. Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings. arXiv preprint arXiv:1704.07130.
  11. Decoupling strategy and generation in negotiation dialogues. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2333–2343, Brussels, Belgium. Association for Computational Linguistics.
  12. Matthew S Henderson. 2015. Discriminative methods for statistical spoken dialogue systems. Ph.D. thesis, University of Cambridge.
  13. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1–38.
  14. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  15. Samuel Louvan and Bernardo Magnini. 2020. Recent neural methods on slot filling and intent classification for task-oriented dialogue systems: A survey. arXiv preprint arXiv:2011.00564.
  16. Amogh Mannekote. 2023. Towards a neural era in dialogue management for collaboration: A literature survey. arXiv preprint arXiv:2307.09021.
  17. Shikib Mehri and Maxine Eskenazi. 2021. Schema-guided paradigm for zero-shot dialog. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 499–508.
  18. Star: A schema-guided dialog dataset for transfer learning. arXiv preprint arXiv:2010.11853.
  19. Tim Paek and Roberto Pieraccini. 2008. Automating spoken dialogue management design using machine learning: An industry perspective. Speech communication, 50(8-9):716–729.
  20. Towards socially intelligent agents with mental state transition and human value. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 146–158.
  21. Developing production-level conversational interfaces with shallow semantic parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 157–162.
  22. Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8689–8696.
  23. Implementation of an inquisitive chatbot for database supported knowledge bases. sādhanā, 41:1173–1178.
  24. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
  25. Context-aware language modeling for goal-oriented dialogue systems. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2351–2366, Seattle, United States. Association for Computational Linguistics.
  26. Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.
  27. The interplay of a conversational ontology and ai planning for health dialogue management. In Proceedings of the 36th annual ACM symposium on applied computing, pages 611–619.
  28. Learning to speak and act in a fantasy text adventure game. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 673–683, Hong Kong, China. Association for Computational Linguistics.
  29. Julio Vizcarra and Kristiina Jokinen. 2022. Knowledge-based dialogue system for the ageing support on daily activities. In International Conference on Human-Computer Interaction, pages 122–133. Springer.
  30. Generative pretraining for paraphrase evaluation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4052–4073, Dublin, Ireland. Association for Computational Linguistics.
  31. Retrieve and refine: Improved sequence generation models for dialogue. arXiv preprint arXiv:1808.04776.
  32. Are we there yet?-a systematic literature review on chatbots in education. Frontiers in artificial intelligence, 4:654924.
  33. Graph transformer networks. Advances in neural information processing systems, 32.
  34. Jing Zhang and Yujin Wang. 2022. SRCB at SemEval-2022 task 5: Pretraining based image to text late sequential fusion system for multimodal misogynous meme identification. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 585–596, Seattle, United States. Association for Computational Linguistics.
  35. Sgd-qa: Fast schema-guided dialogue state tracking for unseen services. arXiv preprint arXiv:2105.08049.
  36. I cast detect thoughts: Learning to converse and guide with intents and theory-of-mind in dungeons and dragons. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11136–11155.

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