Mining Implicit Relevance Feedback from User Behavior for Web Question Answering

• URL: http://arxiv.org/abs/2006.07581v2
• Date: Tue, 16 Jun 2020 01:10:48 GMT
• Title: Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
• Authors: Linjun Shou, Shining Bo, Feixiang Cheng, Ming Gong, Jian Pei, Daxin Jiang
• Abstract summary: 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.
• Score: 92.45607094299181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training and refreshing a web-scale Question Answering (QA) system for a multi-lingual commercial search engine often requires a huge amount of training examples. One principled idea is to mine implicit relevance feedback from user behavior recorded in search engine logs. All previous works on mining implicit relevance feedback target at relevance of web documents rather than passages. Due to several unique characteristics of QA tasks, the existing user behavior models for web documents cannot be applied to infer passage relevance. In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA. We conduct extensive experiments on four test datasets and the results show 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, especially for languages with low resources. Our techniques have been deployed in multi-language services.

Related papers

• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Retrieval-Generation Synergy Augmented Large Language Models [30.53260173572783]
  We propose an iterative retrieval-generation collaborative framework. We conduct experiments on four question answering datasets, including single-hop QA and multi-hop QA tasks.
  arXiv  Detail & Related papers  (2023-10-08T12:50:57Z)
• A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback [12.712416630402119]
  Interactive search can provide a better experience by incorporating interaction feedback from the users. Existing state-of-the-art (SOTA) systems use reinforcement learning (RL) models to incorporate the interactions. Yet such feedback requires extensive RL action space exploration and large amounts of annotated data. This work proposes a new deep Q-learning (DQ) approach, DQrank.
  arXiv  Detail & Related papers  (2023-10-03T18:45:21Z)
• Unified Embedding Based Personalized Retrieval in Etsy Search [0.206242362470764]
  We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end. Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
  arXiv  Detail & Related papers  (2023-06-07T23:24:50Z)
• Utilizing Background Knowledge for Robust Reasoning over Traffic Situations [63.45021731775964]
  We focus on a complementary research aspect of Intelligent Transportation: traffic understanding. We scope our study to text-based methods and datasets given the abundant commonsense knowledge. We adopt three knowledge-driven approaches for zero-shot QA over traffic situations.
  arXiv  Detail & Related papers  (2022-12-04T09:17:24Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• Distantly Supervised Transformers For E-Commerce Product QA [5.460297795256275]
  We propose a practical instant question answering (QA) system on product pages of ecommerce services. For each user query, relevant community question answer (CQA) pairs are retrieved. Our proposed transformer-based model learns a robust relevance function by jointly learning unified syntactic and semantic representations.
  arXiv  Detail & Related papers  (2021-04-07T06:37:16Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.