Automated Query Generation for Evidence Collection from Web Search Engines

• URL: http://arxiv.org/abs/2303.08652v1
• Date: Wed, 15 Mar 2023 14:32:00 GMT
• Title: Automated Query Generation for Evidence Collection from Web Search Engines
• Authors: Nestor Prieto-Chavana, Julie Weeds, David Weir
• Abstract summary: It is widely accepted that so-called facts can be checked by searching for information on the Internet. This process requires a fact-checker to formulate a search query based on the fact and to present it to a search engine. We ask the question as to whether it is possible to automate the first step, that of query generation.
• Score: 2.642698101441705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is widely accepted that so-called facts can be checked by searching for information on the Internet. This process requires a fact-checker to formulate a search query based on the fact and to present it to a search engine. Then, relevant and believable passages need to be identified in the search results before a decision is made. This process is carried out by sub-editors at many news and media organisations on a daily basis. Here, we ask the question as to whether it is possible to automate the first step, that of query generation. Can we automatically formulate search queries based on factual statements which are similar to those formulated by human experts? Here, we consider similarity both in terms of textual similarity and with respect to relevant documents being returned by a search engine. First, we introduce a moderate-sized evidence collection dataset which includes 390 factual statements together with associated human-generated search queries and search results. Then, we investigate generating queries using a number of rule-based and automatic text generation methods based on pre-trained large language models (LLMs). We show that these methods have different merits and propose a hybrid approach which has superior performance in practice.

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)
• BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval [54.54576644403115]
  Many complex real-world queries require in-depth reasoning to identify relevant documents. We introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding.
  arXiv  Detail & Related papers  (2024-07-16T17:58:27Z)
• 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)
• ExcluIR: Exclusionary Neural Information Retrieval [74.08276741093317]
  We present ExcluIR, a set of resources for exclusionary retrieval. evaluation benchmark includes 3,452 high-quality exclusionary queries. training set contains 70,293 exclusionary queries, each paired with a positive document and a negative document.
  arXiv  Detail & Related papers  (2024-04-26T09:43:40Z)
• Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation [16.170841777591345]
  In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. We first take queryd with the query-based summary and the document summary without query as the input of topic relevance model. Then, we utilize the language understanding and generation abilities of large language model (LLM) to rewrite and generate query from queries and documents in existing training data.
  arXiv  Detail & Related papers  (2024-04-03T10:05:47Z)
• Generative Retrieval as Multi-Vector Dense Retrieval [71.75503049199897]
  Generative retrieval generates identifiers of relevant documents in an end-to-end manner. Prior work has demonstrated that generative retrieval with atomic identifiers is equivalent to single-vector dense retrieval. We show that generative retrieval and multi-vector dense retrieval share the same framework for measuring the relevance to a query of a document.
  arXiv  Detail & Related papers  (2024-03-31T13:29:43Z)
• ConvGQR: Generative Query Reformulation for Conversational Search [37.54018632257896]
  ConvGQR is a new framework to reformulate conversational queries based on generative pre-trained language models. We propose a knowledge infusion mechanism to optimize both query reformulation and retrieval.
  arXiv  Detail & Related papers  (2023-05-25T01:45:06Z)
• CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion [68.19934563919192]
  We propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.
  arXiv  Detail & Related papers  (2022-12-18T15:57:46Z)
• 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)
• Leveraging Cognitive Search Patterns to Enhance Automated Natural Language Retrieval Performance [0.0]
  We show that cognitive reformulation patterns that mimic user search behaviour are highlighted. We formalize the application of these patterns by considering a query conceptual representation. A genetic algorithm-based weighting process allows placing emphasis on terms according to their conceptual role-type.
  arXiv  Detail & Related papers  (2020-04-21T14:13:33Z)

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.