@inproceedings{mourya-etal-2026-direct,
    title = "{DIRECT}: Directional Relevance in Conversational Trajectories",
    author = "Mourya, Anshuman  and
      Mukherjee, Rajdeep  and
      Jolly, Prerna  and
      Puranik, Vinayak S  and
      Kaveri, Sivaramakrishnan R",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.71/",
    pages = "948--957",
    ISBN = "979-8-89176-384-5",
    abstract = "Conversational Agents have become ubiquitous across application domains, such as, shopping assistants, medical diagnosis, autonomous task planning etc. Users interacting with these agents often fail to understand how to start a conversation or what to ask next to obtain the desired information. To enable seamless and hassle-free user-agent interactions, we introduce Next Question Suggestions (NQS), which are essentially highly relevant follow-up question recommendations that act as conversation starters or discover-ability tools to capture non-trivial user intents, leading to more engaging conversations. Relying on LLMs for both response as well as NQS generation is a costly ask in latency-constrained commercial settings, with an added risk of handling potentially unsafe or unanswerable generated queries. A key component of building an efficient low-latency NQS experience is, therefore, retrieval (or embedding) models that fetch the most-relevant candidate questions from an offline pre-curated Question Bank (QB). Off-the-shelf embedding models cannot capture domain-specific nuances and more importantly the directionality inherent in follow-up question recommendations. In this work, we propose an end-to-end retrieval system, DIRECT that is optimized to model directional relevance. Given a user query, it produces a ranked list of highly relevant follow-up question recommendations within 1 sec. Our system also contains an LLM-as-a-judge component, tuned on proprietary user-agent interaction logs, to evaluate the end-to-end performance in terms of CTR."
}