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Jul 8, 2024 | 
amazon.science | Marcus Collins |Eugene Agichtein |Oleg Rokhlenko |Shervin Malmasi
Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated LLM chatbots, dialogue managers, response rankers, and recommenders. The problem is worse for multi-modal interactions in realistic conversational product search and recommendation.
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May 17, 2024 | 
amazon.science | Jason Choi |Marcus Collins |Eugene Agichtein |Oleg Rokhlenko
Conversational AI is a subtype of Human-Computer Interaction that has gained wide adoption. These systems are typically powered by Large Language Models (LLMs) that use Retrieval Augmented Generation (RAG) to infuse external knowledge, which is effective against issues like hallucination. However, automatically evaluating retrieval augmented conversations with minimal human effort remains challenging, particularly in online settings.
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May 10, 2024 | 
amazon.science | Nikhita Vedula |Giuseppe Castellucci |Eugene Agichtein |Oleg Rokhlenko
Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting, and enjoyable for CTA users is not trivial, especially for time-consuming or challenging tasks.
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Jan 18, 2024 | 
arxiv.org | Besnik Fetahu |Oleg Rokhlenko |Shervin Malmasi
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Dec 6, 2023 | 
amazon.science | Besnik Fetahu |Zhiyu Chen |Oleg Rokhlenko |Shervin Malmasi
E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key product aspects. This results in a gap between such unnatural product titles and how customers refer to them. It also limits how e-commerce stores can use these seller-provided titles for recommendation, QA, or review summarization.
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Sep 5, 2023 | 
amazon.science | Shervin Malmasi |Oleg Rokhlenko |Eugene Agichtein |Tryambak Gangopadhyay
A key application of conversational search is reining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it infeasible to examine a wide range of user behaviors.
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Aug 31, 2023 | 
amazon.science | Zhiyu Chen |Besnik Fetahu |Oleg Rokhlenko |Shervin Malmasi
Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading to bad user experiences. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity.
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Aug 31, 2023 | 
amazon.science | Zhiyu Chen |Jason Choi |Besnik Fetahu |Oleg Rokhlenko
Customers interacting with product search en-gines are increasingly formulating information-seeking queries. Frequently Asked Ques-tion (FAQ) retrieval aims to retrieve common question-answer pairs for a user query with question intent. Integrating FAQ retrieval in product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support.
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Aug 31, 2023 | 
amazon.science | Eugene Agichtein |Yoelle Maarek |Oleg Rokhlenko
As people learn to interact with AI assistants such as Alexa, their needs change and become more complex. Alexa must evolve accordingly, and offer more sophisticated experiences, which require addressing increasingly complex AI research challenges. In the last few years, Amazon has offered university teams a chance to partner with Amazon scientists in order push the boundaries of the state of the art in conversational AI via the Alexa Prize challenge.
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Aug 31, 2023 | 
amazon.science | Shervin Malmasi |Oleg Rokhlenko |Eugene Agichtein
A key application of conversational search is refining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it unfeasible to examine a wide range of user behaviors.
