User Intent Inference for Web Search and Conversational Agents

• URL: http://arxiv.org/abs/2005.13808v2
• Date: Wed, 8 Jul 2020 16:13:38 GMT
• Title: User Intent Inference for Web Search and Conversational Agents
• Authors: Ali Ahmadvand
• Abstract summary: thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain.
• Score: 3.9400263964632836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, since the user utterances or queries can be short, ambiguous, and contextually dependent. To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain, via: 1) Developing a joint learning model to predict search queries' intents and the product categories associated with them, 2) Discovering new hidden users' intents. All the models will be evaluated on the real queries available from a major e-commerce site search engine. The results from these studies can be leveraged to improve performance of various tasks such as natural language understanding, query scoping, query suggestion, and ranking, resulting in an enriched user experience.

Related papers

• Enhanced Facet Generation with LLM Editing [5.4327243200369555]
  In information retrieval, facet identification of a user query is an important task. Previous studies can enhance facet prediction by leveraging retrieved documents and related queries obtained through a search engine. However, there are challenges in extending it to other applications when a search engine operates as part of the model.
  arXiv  Detail & Related papers  (2024-03-25T00:43:44Z)
• Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations [66.16863141262506]
  We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
  arXiv  Detail & Related papers  (2023-10-22T16:14:56Z)
• Graph Enhanced BERT for Query Understanding [55.90334539898102]
  query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks. We propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph.
  arXiv  Detail & Related papers  (2022-04-03T16:50:30Z)
• Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval [9.078765961879467]
  We deal with a scenario of conversational search with mixed-initiative: namely user-asks system-answers, as well as system-asks (clarification questions) and user-answers. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval that is used both for an initial selection of relevant candidate clarification questions, as well as for fine-tuning two deep-learning models for re-ranking these candidates.
  arXiv  Detail & Related papers  (2021-12-14T11:27:16Z)
• Query Understanding via Intent Description Generation [75.64800976586771]
  We propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task.
  arXiv  Detail & Related papers  (2020-08-25T08:56:40Z)
• Deep Search Query Intent Understanding [17.79430887321982]
  This paper aims to provide a comprehensive learning framework for modeling query intent under different stages of a search. We focus on the design for 1) predicting users' intents as they type in queries on-the-fly in typeahead search using character-level models; and 2) accurate word-level intent prediction models for complete queries.
  arXiv  Detail & Related papers  (2020-08-15T18:19:56Z)
• Query Intent Detection from the SEO Perspective [0.34376560669160383]
  We aim to identify the user query's intent by taking advantage of Google results and machine learning methods. A list of keywords extracted from the clustered queries is used to identify the intent of a new given query.
  arXiv  Detail & Related papers  (2020-06-16T13:08:29Z)
• Mining Implicit Relevance Feedback from User Behavior for Web Question Answering [92.45607094299181]
  We make the first study to explore the correlation between user behavior and passage relevance. Our approach significantly improves the accuracy of passage ranking without extra human labeled data. In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine.
  arXiv  Detail & Related papers  (2020-06-13T07:02:08Z)
• JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search [16.31114596864235]
  An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2020-05-28T05:20:00Z)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)
• IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems [80.0781718687327]
  We analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART" IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture.
  arXiv  Detail & Related papers  (2020-02-03T05:59:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.