Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.Comment: AAAI 2019, 10 page

Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models

Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR papers demonstrate "wins" by comparing against weak baselines. This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection. We do not find evidence of an upward trend in effectiveness over time. In fact, the best reported results are from a decade ago and no recent neural approach comes close. Second, we applied five recent neural models to rerank the strong baselines that Lin used to make his arguments. A significant improvement was observed for one of the models, demonstrating additivity in gains. While there appears to be merit to neural IR approaches, at least some of the gains reported in the literature appear illusory.Comment: Published in the Proceedings of the 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019

End-to-end Neural Information Retrieval

In recent years we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines. In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN. With the information retrieval toolkit Anserini, we build a reranking architecture based on various traditional information retrieval models (QL, QL+RM3, BM25, BM25+RM3), including a strong pseudo-relevance feedback baseline: RM3. With the neural network toolkit MatchZoo, we offer an empirical study of a number of popular neural network ranking models (DSSM, CDSSM, KNRM, DUET, DRMM). Experiments on datasets from the TREC Microblog Tracks and the TREC Robust Retrieval Track show that most existing neural network models cannot beat a simple language model baseline. How- ever, DRMM provides a significant improvement over the pseudo-relevance feedback baseline (BM25+RM3) on the Robust04 dataset and DUET, DRMM and MP-HCNN can provide significant improvements over the baseline (QL+RM3) on the microblog datasets. Further detailed analyses suggest that searching social media and searching news articles exhibit several different characteristics that require customized model design, shedding light on future directions

Cross-Domain Sentence Modeling for Relevance Transfer with BERT

Standard bag-of-words term-matching techniques in document retrieval fail to exploit rich semantic information embedded in the document texts. One promising recent trend in facilitating context-aware semantic matching has been the development of massively pretrained deep transformer models, culminating in BERT as their most popular example today. In this work, we propose adapting BERT as a neural re-ranker for document retrieval to achieve large improvements on news articles. Two fundamental issues arise in applying BERT to ``ad hoc'' document retrieval on newswire collections: relevance judgments in existing test collections are provided only at the document level, and documents often exceed the length that BERT was designed to handle. To overcome these challenges, we compute and aggregate sentence-level evidence to rank documents. The lack of appropriate relevance judgments in test collections is addressed by leveraging sentence-level and passage-level relevance judgments fortuitously available in collections from other domains to capture cross-domain notions of relevance. Our experiments demonstrate that models of relevance can be transferred across domains. By leveraging semantic cues learned across various domains, we propose a model that achieves state-of-the-art results on three standard TREC newswire collections. We explore the effects of cross-domain relevance transfer, and trade-offs between using document and sentence scores for document ranking. We also present an end-to-end document retrieval system that integrates the open-source Anserini information retrieval toolkit, discussing the related technical challenges and design decisions