We address some of the limitations of coverage-based search result diversification models, which often consist of separate components and rely on external systems for query aspects. To overcome these challenges, we introduce an end-to-end learning framework called DUB. Our approach preserves the intrinsic interpretability of coverage-based methods while enhancing diversification performance. Drawing inspiration from the information bottleneck method, we propose an aspect extractor that generates query aspect embeddings optimized as information bottlenecks for the task of diversified document re-ranking. Experimental results demonstrate that DUB outperforms state-of-the-art diversification models.

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-resource languages. Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models. In this work, we propose OPTICAL: Optimal Transport distillation for low-resource Cross-lingual information retrieval. To transfer a model from high to low resource languages, OPTICAL forms the cross-lingual token alignment task as an optimal transport problem to learn from a well-trained monolingual retrieval model. By separating the cross-lingual knowledge from knowledge of query document matching, OPTICAL only needs bitext data for distillation training, which is more feasible for low-resource languages. Experimental results show that, with minimal training data, OPTICAL significantly outperforms strong baselines on low-resource languages, including neural machine translation.

It has been shown that the interpretability of search results is enhanced when query aspects covered by documents are explicitly provided. However, existing work on aspect-oriented explanation of search results explains each document independently. These explanations thus cannot describe the differences between documents. This issue is also true for existing models on query aspect generation. Furthermore, these models provide a single query aspect for each document, even though documents often cover multiple query aspects. To overcome these limitations, we propose LiEGe, an approach that jointly explains all documents in a search result list. LiEGe provides semantic representations at two levels of granularity -- documents and their tokens -- using different interaction signals including cross-document interactions. These allow listwise modeling of a search result list as well as the generation of coherent explanations for documents. To appropriately explain documents that cover multiple query aspects, we introduce two settings for search result explanation: comprehensive and novelty explanation generation. LiEGe is trained and evaluated for both settings. We evaluate LiEGe on datasets built from Wikipedia and real query logs of the Bing search engine. Our experimental results demonstrate that LiEGe outperforms all baselines, with improvements that are substantial and statistically significant.

We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to natural language processing and information retrieval such as session-based and conversational information seeking. Previous studies mainly extract set names from knowledge bases which provide highly reliable entity relations, but suffer from limited coverage of entities and set names that represent broad semantic classes. To address these problems, AutoName generates hypernym-anchored candidate phrases via probing a pre-trained language model and the entities' context in documents. Phrases are then clustered to identify ones that describe common concepts among query entities. Finally, AutoName ranks refined phrases based on the co-occurrences of their words with query entities and the conceptual integrity of their respective clusters. We built a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all baselines.

Existing research on cross-lingual retrieval cannot take good advantage of large-scale pretrained language models such as multilingual BERT and XLM. We hypothesize that the absence of cross-lingual passage-level relevance data for finetuning and the lack of query-document style pretraining are key factors of this issue. In this paper, we introduce two novel retrieval-oriented pretraining tasks to further pretrain cross-lingual language models for downstream retrieval tasks such as cross-lingual ad-hoc retrieval (CLIR) and cross-lingual question answering (CLQA). We construct distant supervision data from multilingual Wikipedia using section alignment to support retrieval-oriented language model pretraining. We also propose to directly finetune language models on part of the evaluation collection by making Transformers capable of accepting longer sequences. Experiments on multiple benchmark datasets show that our proposed model can significantly improve upon general multilingual language models in both the cross-lingual retrieval setting and the cross-lingual transfer setting.

In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With experiments conducted on the CLEF collection over four language pairs, we evaluate and provide insight into different neural model architectures, different ways to represent query-document interactions and word-pair similarity distributions in CLIR. This study paves the way for learning an end-to-end CLIR system using CLWEs.

Corpus-based set expansion refers to mining "sibling" entities of some given seed entities from a corpus. Previous works are limited to using either textual context matching or semantic matching to fulfill this task. Neither matching method takes full advantage of the rich information in free text. We present CaSE, an efficient unsupervised corpus-based set expansion framework that leverages lexical features as well as distributed representations of entities for the set expansion task. Experiments show that CaSE outperforms state-of-the-art set expansion algorithms in terms of expansion accuracy.

We develop Hide-n-Seek, an intent-aware privacy protection plugin for personalized web search. In addition to users' genuine search queries, Hide-n-Seek submits k cover queries and corresponding clicks to an external search engine to disguise a user's search intent grounded and reinforced in a search session by mimicking the true query sequence. The cover queries are synthesized and randomly sampled from a topic hierarchy, where each node represents a coherent search topic estimated by both n-gram and neural language models constructed over crawled web documents. Hide-n-Seek also personalizes the returned search results by re-ranking them based on the genuine user profile developed and maintained on the client side. With a variety of graphical user interfaces, we present the topic-based query obfuscation mechanism to the end users for them to digest how their search privacy is protected.