88 citations · 327 across the 17 of their papers we have counts for
21 papers
Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction
Christophe Van Gysel, Maggie Wu, Lyan Verwimp +4
End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires h…
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Zhihong Lei, Xingyu Na, Mingbin Xu +5
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a…
Synthetic Query Generation using Large Language Models for Virtual Assistants
Sonal Sannigrahi, Thiago Fraga-Silva, Youssef Oualil +1
Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-te…
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants
Youyuan Zhang, Sashank Gondala, Thiago Fraga-Silva +1
On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition (ASR) require effective knowledge integration for the challenging entity-rich query recognition. In this…
Space-Efficient Representation of Entity-centric Query Language Models
Christophe Van Gysel, Mirko Hannemann, Ernest Pusateri +2
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the…
A Discriminative Entity-Aware Language Model for Virtual Assistants
Mandana Saebi, Ernest Pusateri, Aaksha Meghawat +1
High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well. However, ASR often performs poorly on VA requests containing named entities.…
Error-driven Pruning of Language Models for Virtual Assistants
Sashank Gondala, Lyan Verwimp, Ernest Pusateri +2
Language models (LMs) for virtual assistants (VAs) are typically trained on large amounts of data, resulting in prohibitively large models which require excessive memory and/or can…
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
Christophe Van Gysel, Manos Tsagkias, Ernest Pusateri +1
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions…
Connecting and Comparing Language Model Interpolation Techniques
Ernest Pusateri, Christophe Van Gysel, Rami Botros +4
In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation. We compare these techniques as well…
Pytrec_eval: An Extremely Fast Python Interface to trec_eval
Christophe Van Gysel, Maarten de Rijke
We introduce pytrec_eval, a Python interface to the tree_eval information retrieval evaluation toolkit. pytrec_eval exposes the reference implementations of trec_eval within Python…
ILPS at TREC 2017 Common Core Track
Christophe Van Gysel, Dan Li, Evangelos Kanoulas
The TREC 2017 Common Core Track aimed at gathering a diverse set of participating runs and building a new test collection using advanced pooling methods. In this paper, we describe…
Neural Networks for Information Retrieval
Tom Kenter, Alexey Borisov, Christophe Van Gysel +3
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approac…
Remedies against the Vocabulary Gap in Information Retrieval
Christophe Van Gysel
Search engines rely heavily on term-based approaches that represent queries and documents as bags of words. Text---a document or a query---is represented by a bag of its words that…
Reply With: Proactive Recommendation of Email Attachments
Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi +4
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterpr…
Neural Vector Spaces for Unsupervised Information Retrieval
Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we le…
Structural Regularities in Text-based Entity Vector Spaces
Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Entity retrieval is the task of finding entities such as people or products in response to a query, based solely on the textual documents they are associated with. Recent semantic…
Semantic Entity Retrieval Toolkit
Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention. In this paper we describe the Semantic Entity Retrieval Tool…
Neural Networks for Information Retrieval
Tom Kenter, Alexey Borisov, Christophe Van Gysel +3
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many differe…
Pyndri: a Python Interface to the Indri Search Engine
Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke
We introduce pyndri, a Python interface to the Indri search engine. Pyndri allows to access Indri indexes from Python at two levels: (1) dictionary and tokenized document collectio…
Learning Latent Vector Spaces for Product Search
Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for expli…
Unsupervised, Efficient and Semantic Expertise Retrieval
Christophe Van Gysel, Maarten de Rijke, Marcel Worring
We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit featu…