Query-Variant Advertisement Text Generation with Association Knowledge

• URL: http://arxiv.org/abs/2004.06438v3
• Date: Thu, 23 Sep 2021 04:26:09 GMT
• Title: Query-Variant Advertisement Text Generation with Association Knowledge
• Authors: Siyu Duan, Wei Li, Cai Jing, Yancheng He, Yunfang Wu, Xu Sun
• Abstract summary: Traditional text generation methods tend to focus on the general searching needs with high frequency. We propose a query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries. Our model can make use of various personalized needs in queries and generate query-variant advertisement texts.
• Score: 21.18443320935013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online advertising is an important revenue source for many IT companies. In the search advertising scenario, advertisement text that meets the need of the search query would be more attractive to the user. However, the manual creation of query-variant advertisement texts for massive items is expensive. Traditional text generation methods tend to focus on the general searching needs with high frequency while ignoring the diverse personalized searching needs with low frequency. In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords. To solve the problem of ignoring low-frequency needs, we propose a dynamic association mechanism to expand the receptive field based on external knowledge, which can obtain associated words to be added to the input. These associated words can serve as bridges to transfer the ability of the model from the familiar high-frequency words to the unfamiliar low-frequency words. With association, the model can make use of various personalized needs in queries and generate query-variant advertisement texts. Both automatic and human evaluations show that our model can generate more attractive advertisement text than baselines.

Related papers

• Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search [32.35446999027349]
  We leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. The proposed model -- Query Representation Alignment Conversational Retriever, QRACDR, is tested on eight datasets.
  arXiv  Detail & Related papers  (2024-07-29T17:14:36Z)
• Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation [67.89838237013078]
  Named entity recognition (NER) models often struggle with noisy inputs. We propose a more realistic setting in which only noisy text and its NER labels are available. We employ a multi-view training framework that improves robust NER without retrieving text during inference.
  arXiv  Detail & Related papers  (2024-07-26T07:30:41Z)
• Improving Retrieval in Sponsored Search by Leveraging Query Context Signals [6.152499434499752]
  We propose an approach to enhance query understanding by augmenting queries with rich contextual signals. We use web search titles and snippets to ground queries in real-world information and utilize GPT-4 to generate query rewrites and explanations. Our context-aware approach substantially outperforms context-free models.
  arXiv  Detail & Related papers  (2024-07-19T14:28:53Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• Leveraging Large Language Models for Multimodal Search [0.6249768559720121]
  This paper introduces a novel multimodal search model that achieves a new performance milestone on the Fashion200K dataset. We also propose a novel search interface integrating Large Language Models (LLMs) to facilitate natural language interaction.
  arXiv  Detail & Related papers  (2024-04-24T10:30:42Z)
• Large Search Model: Redefining Search Stack in the Era of LLMs [63.503320030117145]
  We introduce a novel conceptual framework called large search model, which redefines the conventional search stack by unifying search tasks with one large language model (LLM) All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts. This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack.
  arXiv  Detail & Related papers  (2023-10-23T05:52:09Z)
• 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)
• Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface [0.0]
  Retrieving answers in a quick and low cost manner without hallucinations using Language models is a major hurdle. This is what prevents employment of Language models in knowledge retrieval automation. For commercial search and chat-bot applications, complete reliance on commercial large language models (LLMs) like GPT 3.5 etc. can be very costly.
  arXiv  Detail & Related papers  (2023-10-06T12:44:04Z)
• Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search [27.42717207107]
  Cross-Modal sponsored search displays multi-modal advertisements (ads) when consumers look for desired products by natural language queries in search engines. The ability to align ads-specific information in both images and texts is crucial for accurate and flexible sponsored search. We propose a simple alignment network for explicitly mapping fine-grained visual parts in ads images to the corresponding text.
  arXiv  Detail & Related papers  (2023-09-28T03:43:57Z)
• Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production [98.98161995555485]
  We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine. Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge.
  arXiv  Detail & Related papers  (2023-02-16T01:58:10Z)
• Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text [62.489652395307914]
  We use the data-to-text method as a means for encoding structured knowledge for knowledge-intensive applications, i.e. open-domain question answering (QA) Specifically, we propose a verbalizer-retriever-reader framework for open-domain QA over data and text where verbalized tables from Wikipedia and triples from Wikidata are used as augmented knowledge sources. We show that our Unified Data and Text QA, UDT-QA, can effectively benefit from the expanded knowledge index, leading to large gains over text-only baselines.
  arXiv  Detail & Related papers  (2021-10-16T00:11:21Z)

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.