Domain Adaptation for Enterprise Email Search

Tran, Brandon; Karimzadehgan, Maryam; Pasumarthi, Rama Kumar; Bendersky, Michael; Metzler, Donald

doi:10.1145/3331184.3331204

Cited by 17 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Yilmaz et al (2019) has shown that training models on general-domain corpora adapts well to new domains without targeted supervision. Another common technique for adaptation to specialized domains is to learn cross-domain representations (Cohen et al, 2018;Tran et al, 2019). Our work is more aligned with methods like Yilmaz et al (2019) which use general domain resources to build neural models for new domains, though via different techniques -data augmentation vs. model transfer.…”

Section: Related Workmentioning

confidence: 99%

Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

Ma¹,

Korotkov²,

Yang³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

View full text Add to dashboard Cite

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid termneural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models.

show abstract

Section: Related Workmentioning

confidence: 99%

Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

Ma¹,

Korotkov²,

Yang³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

View full text Add to dashboard Cite

show abstract

“…Shen et al [27] clustered queries based on the frequent n-grams of results retrieved with a baseline ranker and then used the query cluster information as an auxiliary objective in multi-task learning. There has also been research that studies how to combine sparse and dense features in a unified model effectively [22], combine textual information from queries and documents with other side information to conduct effective and efficient learning to rank [24], and transfer models learned from one domain to another in email search [30].…”

Section: Related Workmentioning

confidence: 99%

Leveraging User Behavior History for Personalized Email Search

Metrikov

et al. 2021

Proceedings of the Web Conference 2021

View full text Add to dashboard Cite

An effective email search engine can facilitate users' search tasks and improve their communication efficiency. Users could have varied preferences on various ranking signals of an email, such as relevance and recency based on their tasks at hand and even their jobs. Thus a uniform matching pattern is not optimal for all users. Instead, an effective email ranker should conduct personalized ranking by taking users' characteristics into account. Existing studies have explored user characteristics from various angles to make email search results personalized. However, little attention has been given to users' search history for characterizing users. Although users' historical behaviors have been shown to be beneficial as context in Web search, their effect in email search has not been studied and remains unknown. Given these observations, we propose to leverage user search history as query context to characterize users and build a context-aware ranking model for email search. In contrast to previous context-dependent ranking techniques that are based on raw texts, we use ranking features in the search history. This frees us from potential privacy leakage while giving a better generalization power to unseen users. Accordingly, we propose a context-dependent neural ranking model (CNRM) that encodes the ranking features in users' search history as query context and show that it can significantly outperform the baseline neural model without using the context. We also investigate the benefit of the query context vectors obtained from CNRM on the state-of-the-art learning-to-rank model LambdaMart by clustering the vectors and incorporating the cluster information. Experimental results show that significantly better results can be achieved on LambdaMart as well, indicating that the query clusters can characterize different users and effectively turn the ranking model personalized. CCS CONCEPTS• Information systems → Environment-specific retrieval; Retrieval models and ranking.

show abstract

“…Recently, deep neural networks (DNNs) have shown great success in learning-to-rank tasks. They significantly improve the performance of search engines in the presence of large-scale query logs in both web search [19] and email settings [39,45,51]. The advantages of DNNs over traditional models are mainly two-fold: (1) DNNs have strong power to learn embedded representations from sparse features, including words [33] and characters [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, there have been few efforts that study how to effectively exploit both dense and sparse features in the learningto-rank setting, probably because a natural approach exists-simply concatenating dense features with embedded sparse features and feeding them into the DNNs. Indeed, many previous deep neural email search models use direct concatenation of dense features with embedded sparse features [15,38,39,45].…”

Section: Introductionmentioning

confidence: 99%

Separate and Attend in Personal Email Search

Karimzadehgan

Zhuang

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

Self Cite

View full text Add to dashboard Cite

In personal email search, user queries often impose different requirements on different aspects of the retrieved emails. For example, the query "my recent flight to the US" requires emails to be ranked based on both textual contents and recency of the email documents, while other queries such as "medical history" do not impose any constraints on the recency of the email. Recent deep learning-to-rank models for personal email search often directly concatenate dense numerical features 1 (e.g., document age) with embedded sparse features (e.g., n-gram embeddings). In this paper, we first show with a set of experiments on synthetic datasets that direct concatenation of dense and sparse features does not lead to the optimal search performance of deep neural ranking models. To effectively incorporate both sparse and dense email features into personal email search ranking, we propose a novel neural model, SepAttn. SepAttn first builds two separate neural models to learn from sparse and dense features respectively, and then applies an attention mechanism at the prediction level to derive the final prediction from these two models. We conduct a comprehensive set of experiments on a large-scale email search dataset, and demonstrate that our SepAttn model consistently improves the search quality over the baseline models.

show abstract

Domain Adaptation for Enterprise Email Search

Cited by 17 publications

References 44 publications

Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

Leveraging User Behavior History for Personalized Email Search

Separate and Attend in Personal Email Search

Contact Info

Product

Resources

About