# IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

arXiv: https://arxiv.org/abs/2002.00571 · HTML: https://mlsanity.com/paper/2002.00571/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [On the Calibration and Uncertainty of Neural Learning to Rank Models](https://mlsanity.com/paper/2101.04356/) (2021)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs](https://mlsanity.com/paper/2402.11633/) (2024)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems](https://mlsanity.com/paper/2111.13463/) (2021)
- [A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction](https://mlsanity.com/paper/2408.09815/) (2024)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [Towards End-to-End Open Conversational Machine Reading](https://mlsanity.com/paper/2210.07113/) (2022)
- [BehaveGPT: A Foundation Model for Large-scale User Behavior Modeling](https://mlsanity.com/paper/2505.17631/) (2025)
- [Ethics, Rules of Engagement, and AI: Neural Narrative Mapping Using Large Transformer Language Models](https://mlsanity.com/paper/2202.02647/) (2022)
- [FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems](https://mlsanity.com/paper/2304.00180/) (2023)
- [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023)
- [Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://mlsanity.com/paper/2309.11307/) (2023)
- [Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems](https://mlsanity.com/paper/2110.15235/) (2021)
- [Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues](https://mlsanity.com/paper/2204.10558/) (2022)
- [Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations](https://mlsanity.com/paper/2404.04272/) (2024)

## Abstract (arXiv)

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART", which refers to "Intent-Aware Ranking with Transformers". IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.